code review assignments

Code Review — The Ultimate Guide

by Assaf Elovic

Code Review — The Ultimate Guide

The ultimate guide for building your team’s code review process.

After conducting hundreds of code reviews, leading R&D teams and pushing several unintentional bugs myself, I’ve decided to share my conclusions for building the ultimate code review process for your team.

This article assumes you know what a code review is. So if you don’t, click here for a great intro.

Let’s quickly state some straightforward reasons as to why you should do code reviews:

• Can help reduce bugs in code.
• Validate that all coding requirements have been filled.
• An effective way to learn from peers and get familiar with the code base.
• Helps maintain code styling across the team.
• Team cohesion — encourage developers to talk to each other on best practices and coding standards.
• Improves overall code quality due to peer pressure.

However, code reviews can be one of the most difficult and time-consuming parts of the software development process.

We’ve all been there. You might have waited days until your code was reviewed. Once it was reviewed you started a ping pong with the reviewer of resubmitting your pull request. All the sudden you’re spending weeks going back and forth. You are context switching between new features and old commits that still need polishing.

If the code review process is not planned right, it could have more cost than value.

This is why it’s extremely important to structure and build a well-defined process for code reviews within your engineering team.

In general, you’ll need to have in place well-defined guidelines for both the reviewer and reviewee, prior to creating a pull request and while it’s being reviewed. More specifically:

Define perquisites for creating pull requests.

I’ve found that the following greatly reduces friction:

• Make sure code compiles successfully.
• Read and annotate your code.
• Build and run tests that validate the scope of your code.
• All code in codebase should be tested.
• Link relevant tickets/items in your task management tool (JIRA for example) to your pull request.
• Do not assign a reviewer until you’ve finalized the above.

Define reviewee responsibilities

While the reviewer is last in the chain of merging your PR, the better it’s handed over by the reviewee, the fewer risks you’ll run into in the long term. Here are some guidelines that can greatly help:

• Communicate with your reviewer — Give your reviewers background about your task. Since most of us pull request authors have likely been reviewers already, simply put yourself in the shoes of the reviewer and ask, “How could this be easier for me?”
• Make smaller pull requests — Making smaller pull requests is the best way to speed up your review time. Keep your pull requests small so that you can iterate more quickly and accurately. In general, smaller code changes are also easier to test and verify as stable. When a pull request is small, it’s easier for the reviewers to understand the context and reason with the logic.
• Avoid changes during the code review — Major changes in the middle of code review basically resets the entire review process. If you need to make major changes after submitting a review, you may want to ship your existing review and follow-up with additional changes. If you need to make major changes after starting the code review process, make sure to communicate this to the reviewer as early in the process as possible.
• Respond to all actionable code review feedback — Even if you don’t implement their feedback, respond to it and explain your reasoning. If there’s something you don’t understand, ask questions inside or outside the code review.
• Code reviews are discussions, not dictation — You can think of most code review feedback as a suggestion more than an order. It’s fine to disagree with a reviewer’s feedback but you need to explain why and give them an opportunity to respond.

Define reviewer responsibilities

Since the reviewer is last in the chain before merging the code, a great part of the responsibility is on him for reducing errors. The reviewer should:

• Be aware to the task description and requirements.
• Make sure to completely understand the code.
• Evaluate all the architecture tradeoffs.
• Divide your comments into 3 categories: Critical, Optional and Positive. The first are comments that the developer must accept to change, and the latter being comments that let the developer know your appreciation for nice pieces of code.

Also, avoid many comments and use Github review instead (see example below).

When you have several comments, you should use the review option in Github, instead of comment each of them separately, and notify the developer (PR owner) when you’re done.

Finally, I’ve found that asking the following questions is a great tool for an overall better and easier reviewing process:

• Am I having difficulty in understanding this code?
• Is there any complexity in the code which could be reduced by refactoring?
• Is the code well organized in a package structure which makes sense?
• Are the class names intuitive and is it obvious what they do?
• Are there any classes which are notably large?
• Are there any particularly long methods?
• Do all the method names seem clear and intuitive?
• Is the code well documented?
• Is the code well tested?
• Are there ways in which this code could be made more efficient?
• Does the code meet our teams styling standards?

There are various effective and different code review practices that vary based on team’s needs. So assume this is my personal opinion and that there are other ways that might work for your team. In the end, building such a sensitive process should be subjective to your companies goals, team’s culture and overall R&D structure.

If you have any questions or feedback for improving these guidelines, please feel free to add a comment below!

If this article was helpful, share it .

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

How to conduct code reviews (+ a checklist!)

GitLab’s recent DevSecOps global survey revealed that 60% of developers find code reviews “very valuable” for security and code quality. Respondents also noted that code reviews are a bottleneck, citing strictness, taking too long, finding someone to complete a review, and being unsure how to perform effective code reviews.

This post breaks down how to conduct code reviews efficiently and effectively, so developers can get the value out of them with fewer frustrations. You’ll learn:

• 4 benefits of code reviews 
• 5 best practices to consider
• 4 tips to make your code reviews better 
• 5 code review tools to explore 
• A code review checklist starter pack to help you structure your process

4 benefits of code reviews

Meaningful code reviews provide many benefits for programmers, development teams, and the product’s end-users. Below are four key benefits of creating a consistent code review practice.

1. Code reviews facilitate knowledge sharing.

Many programmers do their work in an isolated, independent environment. While deep work has benefits, resilient teams must share knowledge to withstand team changes, employee resignations, and unplanned time off (aka your bus factor ). Code reviews promote cross-collaboration and encourage developers to interact, teach each other, and use team knowledge to uplevel individual skill sets. Decentralized knowledge can foster colleagues' trust rather than a “mine vs. yours” ownership mentality. 

2. Programmers can identify and address bugs sooner.

The sooner programmers identify and fix bugs, the cheaper it is to fix them. According to Deepsource , the relative cost to fix bugs increases 30x from the requirements phase to the production/post-release phase. Not only is it cheaper to address bugs sooner, but it’s also easier most of the time. The code is still fresh in developers’ minds, and issues may be less complex since the code is in an earlier stage. Addressing bugs produces better software in the long run and allows programmers to optimize code for better performance.

3. Reviews help maintain consistent coding styles.

Developers have unique programming styles, preferences, and specialized skills they bring to the table. Some level of individuality and uniqueness provides better solutions and creates teams who are better at problem-solving. But too much originality can hinder collaboration, stall progress, and create inconsistency in the results. You can use the code review process to ensure developers follow and maintain certain coding practices. This approach standardizes quality across team members and projects. It also helps current and future developers work together in the long run without wasting time trying to dissect the code to get on board.

4. Code reviews promote team cohesion. 

Similar to knowledge sharing, code reviews offer the opportunity to reduce working in silos while enabling teamwork and cross-collaboration. Benefits of teamwork in software development include improved code quality, enhanced creativity, elevated efficiencies, improved skills, enhanced business potential, and transparency. Code reviews are one tool you can implement and reap ongoing benefits from in the long term. 

5 code review best practices 

Not all code review strategies are successful. Structuring code reviews requires intention, thoughtful planning, and iteration to create the strategy that works best for your team. Below are five code review best practices to consider when implementing or revising your code review process:

1. Keep reviews manageable in size and length.

Effective peer code reviews aim for quantity over quality, which is why you should limit the number of lines of code (LOC) for review in one sitting. If you spend too much time reviewing code in one session or review too many lines of code, your review may be less effective and thorough (which defeats the purpose of a review). Our brains can only process so much information at once without losing interest or being able to give it our best efforts.

Smartbear conducted a study of a Cisco Systems programming team and found that developers should review no more than 200 to 4000 lines of code at one time. Beyond 400 LOC, developers’ ability to discover defects diminishes. 

Along the same lines, industry experts recommend being mindful of how much time you spend conducting a code review to yield the best results. Software developer Kathryn Hodge recommends spending no more than 60 to 90 minutes completing a code review. This falls in line with productivity research that suggests the most productive people work for approximately 52 minutes at a time, then take a break for 17 minutes. 

There are no hard-and-fast rules for structuring code reviews. What’s most important is finding a structure that works well for you and your teams and avoiding spending too much time and energy in exchange for poor results.

2. Compare code against standards, not personal preferences.

Don’t let code reviews become a platform for inserting nitpicky personal preferences in place of valuable feedback. The focus should be on meeting standards and preserving quality to get the most out of code reviews. To do this successfully, managers and organizations must supply developers with agreed-upon standards and perhaps a checklist to leverage during the review.

Managers and tenured developers should also teach new team members what they’re looking for in code reviews. For example, consider conducting team training encouraging developers to avoid getting nitpicky unless the changes negatively impact the code’s functionality. Emphasize your team’s coding standards so team members become familiar with them and know what they should keep an eye out for. In addition to standards, teams can use automated tools for quality and consistency.

One helpful suggestion for avoiding inserting personal preferences in a review is to ask the author if you can meet at a separate time for a knowledge share. This way, teammates can have healthy, conversational debates and share knowledge and best practices without stalling the current codebase. 

3. Provide constructive, concise, and actionable feedback.

Reviewers should provide neutral feedback and focus on improving the code. Additionally, reviewers should avoid judging the author and leaving vague comments. Constructive, concise, and actionable feedback will help the author of the code learn something new, make beneficial changes, and point them in the right direction rather than leave them guessing.

As a reviewer, consider the following when offering feedback:

Distinguish between required changes and suggestions in your feedback.  

Not all comments and remarks fall into the same category. It’s beneficial to call out what type of feedback you’re offering to ensure the author understands your expectations following the review. 

Explain the “why” in your remarks

Comments like, “This LOC is wrong and doesn’t make sense,” aren’t helpful or concise. The author receiving the feedback needs to know what about the LOC is incorrect, why it’s wrong, and what steps they can take to get back on track. Make it a point to explain why you’re leaving the comment that you are for learning opportunities, fewer follow-ups, and avoided frustrations. 

Provide actionable insights and learning lessons. 

Code reviews should help the code author learn how to do things differently and better next time. In your feedback, consider including additional reading resources when it makes sense. Provide links to pointers, lessons, and company documentation for reference. 

One final tip for building a constructive feedback code review culture: Teach developers to ask for specific feedback. “Be clear about what you want feedback on. When sending out a request for review, be specific. This will help focus the reviewer’s attention and ensure that they’re looking at the right things,” said Matt Post, programmer, and co-founder of WCAG Pros .

4. Rotate code reviewers.

Avoid falling into the trap of leveraging tenured developers as reviewers. Instead, involve everyone in the process. Carry the mindset that senior developers need to have their code reviewed just as much as entry-level developers. 

One way to implement a rotational process is to implement an automation tool to assign reviews. For example, on GitHub, users can leverage routing algorithms in which code reviews assignments automatically choose and assign reviewers through a round robin or load balance workload. 

5. Use a code review checklist to standardize the process.

Create a predetermined set of questions for team members to follow during the code review process for an added layer of consistency. A code review checklist adds a structured approach, so authors feel that their teammates are reviewing their work fairly across the board. Some categories to include in your code review checklist are readability, security, architecture, and reusability (and more on checklists later!)

4 tips for better code reviews

Spoiler alert: no code review is perfect. There are always areas for improvement and opportunities to deploy new tactics to test. Spice up your code review process with these four tips for doing code reviews better:

1. Incorporate a “what and why” review framework. 

Surface-level feedback and conducting code reviews to check a box aren’t valuable foundations for your code review process. If your development team currently provides feedback on what to change but isn’t explaining why the author should make a change, they’re missing an opportunity to grow their skills. 

A “what and why” review framework teaches teams how to suggest changes and explain their position, further enhancing their development knowledge and communication skills. Empower your team members to avoid accepting suggestions without understanding the reasoning behind them to build this explanatory nature into your coding culture.

2. Adjust code reviews in the context of the codebase.

Depending on the structure of your team and the nature of your work, consider tailoring code reviews. Specific, tailored reviews, particularly around the client’s asks, enables teams to provide better products to clients by double-checking that they’re meeting requirements. 

Take caution when implementing a tailored practice, especially if your team is low or short on resources, as this can require more time and effort. Conduct a standard review for quality and consistency purposes, and consider adding an additional checklist to take the review deeper if the codebase allows.

3. Teach team members how to be empathetic. 

Providing feedback can be harmful and unpleasant if you don’t execute the review process properly. Encourage team members to provide input, remove personal judgments, and remember that the author dedicated time and effort to writing their code . Organizations should prioritize creating psychologically safe environments with healthy working relationships for team members to engage in these activities meaningfully. Managers should also encourage positive remarks in addition to areas of opportunity for improvement to make the process as successful as possible.

4. Implement an open feedback loop.

Code reviews don’t need to be stagnant (however, when you find something that works well—leave it alone). Conduct post-review surveys with code authors and reviewers to improve your code reviews. On a recurring cadence such as quarterly, obtain feedback from all team members participating in code reviews. Be iterative and implement updates based on team feedback.

5 code review tools to try

Code review tools are a great resource to consider as part of the review process. If you aren’t sure where to start, here’s a list of five code review tools to get you started, listed in alphabetical order.

1. Azure Repos

Azure Repos is an Azure DevOps service enabling collaborative code reviews. With Azure Repos, you can perform more effective Git code reviews with threaded discussions, built-in continuous integration/continuous delivery (CI/CD) capabilities, and code quality mechanisms. 

2. Bitbucket 

Bitbucket’s code review process supports team collaboration, enables teams to find bugs faster, and allows them to merge confidently. With a one-page view, developers reduce context switching and can focus on improving the code.

3. Collaborator 

Smartbear’s Collaborator tool offers comprehensive review capabilities, proof of review with electronic signatures, integrations with other major development tools like GitHub and GitLab , and real-time threaded conversations. 

GitHub built lightweight code review tools into their pull requests. Team members can see every update, discuss code in comments and reviews, resolve conflicts, and merge the highest quality code. 

GitLab’s code review process streamlines code review and approvals by centralizing the review and approval workflows. Noteworthy features include file attachments (so you can communicate beyond text), threaded discussions, bulk edit merge requests, custom notifications, approval rules, and more. 

Code review checklist starter pack

For effective code reviews, you’ll want to tailor your checklist and validate that it’s relevant to your teams and specific products. Below is a code review checklist starter pack, including some examples of recommended questions to help you create your own.

• Does this code change do what the author intended it to?
• Can we simplify this solution?
• Is the code scalable?
• Is the code easy to test?
• Will any events or inputs break the code if we implement the change?
• Does the code change meet our agreed-upon team/organization standards?
• Did the author update the appropriate documentation?
• Is there any risk of this code change negatively impacting performance?
• Is the code easy to understand?
• Will this code change impact other teams, and should they also review the change?

Here’s to better code

The code review process is great for sharing knowledge, fixing bugs, and ensuring consistent code quality. Code reviews should be manageable, constructive, and involve all developers as the author and reviewer. Specific feedback, contextual adjustments, encouraging empathy, and responding to developer feedback on the process can help improve your code reviews. Consider using a code review tool and a standardized checklist to streamline your process. Happy coding!

About the author

Alyssa Towns is a freelance writer for Clockwise based in Denver, CO. She works in communications and change management. She primarily writes productivity and career-adjacent content and has bylines in G2, The Everygirl, Insider, and other publications. When she isn't writing, Alyssa enjoys trying new restaurants with her husband, playing with her Bengal cats, adventuring outdoors, or reading a book from her TBR list.

Subscribe to our monthly newsletter

Look out for a confirmation email in your inbox

Optimize your work day with AI powered calendar automation.

Make your schedule work for you, more from clockwise.

Optimizing DevOps feedback loops in software development

11 Developer experience tools and strategies to improve DX

AI Tools for Developers & Software Engineers

The Absolute Best Note Taking Apps for Mac

September 23, 2022

Code review best practices on our team

Jonathan Bender

How Stashpad and other startups engage the whole team to build a code review culture that works.

Code reviews might feel like bumper-to-bumper traffic on your morning commute. You’re stuck. You just need to get through it in order to get to your destination.

Code reviews can sap the joy out of your day. Steal focus. Make it hard to concentrate, even if (in reality) you haven’t left your desk all morning .

As a result, you could have a tendency to try and rush through a list of pull requests. Or put them off until they threaten to get in the way of moving a feature forward. But with both approaches you’re missing an opportunity to strengthen the culture of your team.

For Derek Prior, a staff engineering manager at GitHub, code reviews are how you build the right culture at your company.

“Code review is the discipline of discussing your code with your peers that drives a higher standard of coding,” said Prior at RailsConf.

Those discussions around code give you insight into your team, encourage knowledge sharing, and drive innovation. You’re not stuck in code reviews. They’re the path to getting unstuck.

Code reviews can be a grind. They can bring out our worst habits. But, they can also help you understand the needs of your team and forge deeper connections.

By building a good code review culture, you will make a better culture for your team. Here’s how to do it.

1. Find your code review bottleneck.

Before you can change the code review process, you have to understand where it is breaking down. There’s always a bottleneck with code reviews. And if you’re a founder or senior engineer, it may very well be you.

Developers with the most experience are the ones on your team most likely to be asked to check out code. It’s a lot of work. And a lot of weight. A backlog of reviews might even be blocking progress in other areas like shipping a new feature.

Maybe you’ve already recognized the problem. So, you started to train someone. But training takes time and trust. Meanwhile, your backlog of pull requests is growing.

Change begins with acknowledging that someone on your team is carrying too much of the burden. Remember, a senior engineer is often not the only one who can review a piece of code.

Your team wants more ownership. And you’re probably ready to clear your plate. So, how do you lighten your load and give them the opportunity?

2. Assess your team’s attitude and comfort with code reviews.

When you notice that all of your code reviews are being done by the same person or a short list of people, Uma Chingunde, VP of Engineering at Render , recommends asking this question:

“Is everyone feeling confident to do code reviews?”

Even the most functional teams struggle with how to comment in code reviews. In the early stages of remaking your code review process, encourage teammates to remember there’s a human behind the code.

Gergely Orosz, the developer behind the blog and newsletter The Pragmatic Engineer, believes empathy plays a big role in better code review culture .

Start by acknowledging that members of your team might still be learning your guidelines or parts of the code. Then, he recommends focusing on “explaining alternative approaches,” and submitting reviews that are “very positive in tone, celebrating the first few changes to the codebase that the author is suggesting.”

There are lots of reasons why members on your team aren’t taking on code reviews. You won’t know why unless you ask. Here’s how Chingunde suggests digging into what’s happening.

“If someone on the team doesn’t review, then have a chat,” said Chingunde. “You can say, ‘I notice you don’t review.’ It’s possible they don’t feel confident or they don’t get the value.”

Your teammate’s answer will let you know if you need to look at how you’re communicating, sharing knowledge, or assigning code reviews. Spoiler alert: It’s all of the above.

3. Good communication begins with clear, concise pull requests.

Ambiguity is a quick way to side track code reviews. The best outcomes – whether it’s code reviews or brainstorming – come from asking distinct, meaningful questions.

The team at Palantir, in a blog post about code review best practices , has developed a practical method for streamlining the code review process.

“"Only submit complete, self-reviewed (by diff), and self-tested CRs,” notes the Palantir team. “In order to save reviewers’ time, test the submitted changes (i.e., run the test suite) and make sure they pass all builds as well as all tests and code quality checks, both locally and on the CI servers, before assigning reviewers."

Take the time to catch the things that are obvious. You’ll make it easier for the reviewer to get at what’s important and you’ll benefit from insight into challenges you can’t solve on your own.

For Prior, the pull request is the opening of a conversation. The request author provides context – he suggests two paragraphs (focusing on what you’ve learned) for each change – to help the reviewer understand why a given code choice was made. That explanation is also relevant to future discussions because it becomes part of the commit.

The reviewer of the code, in turn, should “ask questions rather than making demands.” By framing potential changes as questions, you’ll encourage discussion that makes space for multiple perspectives and the chance to share knowledge about why a given idea could be the best way forward.

4. Track how long it takes for code reviews to be completed.

As you begin to incorporate more of your team into the code review process, things could naturally slow down.

So, what should you do if code reviews are taking too long? Chingunde thinks this is a great time for another check in with your engineers.

“If people take too long to do reviews, is it because they’re too busy doing their own thing? Or is it because they find it hard to review the code?”

It’s typically some combination of other responsibilities and unclear expectations that may lead to a slower pace of code reviews. In order to overcome that inertia, you will have to intentionally build time into your team’s schedule and reassess how you assign code reviews.

5. Optimize your schedule by using pause points for code reviews.

Code reviews don’t have to be a drag on productivity. By building in time around natural pause points, you can avoid disrupting your flow and the cost of context switching. Take the 30 minutes before lunch or after the morning stand-up to review code.

Regular time set aside for reviews minimizes begins to build a habit. You might even use whatever comes next – an espresso or walk around the block – as a reward for completing a code review while you’re establishing that habit.

This strategy is a way to effectively use the time when you’re transitioning away from meetings or coming out of deep work. If it’s following a stand up meeting, the code review could even begin with a short dialogue with the author of a pull request to help give you additional context or emphasis beyond what’s been typed.

6. Assign code reviews to establish expectations for your team.

Your team might be operating under the assumption that code reviews aren’t their responsibility. Asking for someone to contribute isn’t enough to break that assumption. You have to assign code reviews to each member of your team to make it clear this is a shared responsibility.

There are a number of ways to structure assignments. Here are two ideas that could work for your team: Pair engineers together on tasks so you don’t need separate reviews or assign reviews via a rotation system.

Pairs are a useful construct because they facilitate dialogue. And you don’t need to explicitly assign someone to a review. You write each piece of logic as a pair – one person “driving” (typing) – as the other person reads and discusses their code. There will inevitably be more ideas shared and different approaches.

You’re introducing a partner to speed up the pace of play. While pairing, you automatically have a built-in code review as you write the code together. That knowledge – that someone will back you up – also alleviates the stress of feeling like you have to handle everything.

We’re currently evaluating automatic code review assignments . Creating an automatic assignment rotation helps both the author of a pull request and the assignee. Make sure there’s a clear way for team members to tell everyone, whether in Slack or somewhere else, that they’ve claimed a code review.

An automated system reduces decision fatigue for your engineers. It eliminates the feeling that code review may be a burden and the cost of trying to remember who you last tapped for a review assignment. Perhaps, most importantly, it supports a culture of code reviewing by making it clear that everyone will take a turn.

You can always add in an option to override the automatic assignment if you need someone on your team with specific skills to review your code.

Building a sustainable code review culture encourages productive debates and unexpected insights. Team ownership of code strengthens relationships by defusing conflicts and preventing burnout.

Your team might get by without a well-defined code review process. But establishing clear practices and expectations around code review can help your team better share the load and move forward more smoothly and efficiently.

Photo by renee_ek on Unsplash.

Make your Work Note-Worthy

Also available for .

Stay In Touch

Subscribe to the Stashpad Newsletter to receive occasional product updates and company announcements.

Stashpad Docs

Stashpad Lists

Meet the Team

Copyright Caeli Inc. 2024

Code review assignment (beta)

Teams can now be configured to assign a specified number of reviewers when a team is requested for code review. When coupled with CODEOWNERS , organizations can now ensure that code is reviewed by the proper team and automate distribution of code reviews across team members. Code review assignment is available for all users who are members of an organization as public beta.

Learn more about code review assignment on GitHub

GitHub Packages is generally available

Starting today, GitHub Packages (formerly GitHub Package Registry) is generally available. The use of GitHub Packages is free for all public repositories, and every plan gets included storage and data transfer for private repositories.

Learn more about GitHub Packages pricing

GitHub Actions is generally available

Starting today, GitHub Actions is generally available. GitHub Actions are free for all public repositories, and every plan gets included storage and runner minutes for private repositories.

Learn more about GitHub Actions pricing

Subscribe to our newsletter

Code with confidence. Discover tips, technical guides, and best practices in our biweekly newsletter just for devs.

• Developer Tools
• Product Engineering

Best Practices for Code Review and the Top Code Review Tools.

--> By  [x]cube LABS

--> Published: Oct 26 2023

Code quality assurance is of the utmost importance in the quick-paced world of software development. You’ve probably heard of the term “code review.” This essential phase can determine a project’s success. However, did you know that there are practical tools for code review that can improve and speed up this crucial procedure?

In this blog post, we’ll dive deep into the realm of code review tools and best practices. We’ll explore code review, why it’s so crucial, and how using the right tools can revolutionize your development workflow. Whether you’re part of a large development team or working on a solo project, understanding code review tools and best practices can significantly impact the quality of your code and the overall success of your software projects.

Introduction:

So, what is code review? It is a fundamental process in software development, serving as a critical quality assurance step. It involves systematically examining code changes to identify issues, ensure adherence to coding standards, and promote collaboration among team members. Code review tools are pivotal in this process, enabling efficient and effective code inspections. 

A. The Importance of Code Reviews in Software Development:

Code reviews are indispensable for several reasons:

Quality Assurance : Code reviews catch bugs, logic errors, and security vulnerabilities early in the development cycle, reducing the cost of fixing issues later.

Knowledge Sharing : They promote knowledge sharing and foster collaboration among team members. Developers can learn from each other’s code and best practices.

Consistency : Code reviews ensure consistency in coding style and adherence to coding standards, enhancing code readability and maintainability.

Code Ownership : They distribute code ownership among team members, reducing the risk of a single point of failure.

Continuous Improvement : Feedback from code reviews helps developers improve their coding skills and make informed decisions.

Also Read: How to Automate Your Software Composition Analysis?

B. Role of Code Review Tools:

Code review tools are software applications designed to streamline and facilitate code review. Their essential functions include:

Code Diffing : Code review tools highlight the differences between the new code and the existing codebase, making it easier for reviewers to identify changes.

Commenting and Feedback : They allow reviewers to leave comments directly in the code, facilitating discussions and clarifications between developers.

Automated Checks : Some tools offer automated checks for code quality, security vulnerabilities, and adherence to coding standards.

Version Control Integration : Code review tools often integrate with version control systems (e.g., Git, SVN), making it seamless to initiate and track code reviews within the development workflow.

Workflow Management : They provide workflow management features to assign reviewers, set review priorities, and track the progress of reviews.

C. Focus on Code Review Tools and Best Practices:

To ensure that your code review process is optimized, consider the following best practices:

Establish Clear Guidelines : Define clear and concise code review guidelines that specify what should be reviewed and the expected level of detail.

Regular Code Reviews : Make code reviews a normal development process. Frequent checks catch issues early.

Use Specialized Tools : Invest in code review tools that suit your team’s needs and integrate well with your development environment.

Include All Relevant Team Members : Ensure that all relevant team members, including developers, testers, and product owners, participate in code reviews.

Constructive Feedback : Provide feedback that is specific, actionable, and respectful. Focus on improving code quality, not criticizing the author.

Automated Checks : Leverage automated code analysis tools to catch common issues and enforce coding standards.

Continuous Learning : Encourage a culture of constant learning and improvement by discussing lessons learned from code reviews.

Why do Code Reviews Matter?

Ensuring Code Quality:

• Code reviews are fundamental for identifying and rectifying bugs, code smells, and potential security vulnerabilities.
• Code review tools automate the code analysis process, helping developers catch issues early in the development cycle.
• Code review tools contribute to creating robust and reliable software by maintaining code quality standards.

Knowledge Sharing and Collaboration:

• Code reviews foster collaboration among team members by providing a platform for constructive feedback and discussions.
• Code review tools enable developers to leave comments, suggestions, and annotations directly within the codebase, making communication seamless.
• Collaboration facilitated by these tools enhances team cohesion and knowledge sharing, resulting in better-informed developers.

Code Consistency:

• Maintaining coding standards and consistency across a project is crucial for readability and maintainability.
• Code review tools can enforce coding guidelines and style standards, ensuring all team members adhere to best practices.
• Consistency achieved through these tools leads to a more straightforward way of understanding and maintaining code.

Risk Mitigation:

• Code reviews and tools help mitigate the risk of introducing critical bugs or security vulnerabilities into production code by catching issues early.
• Code review tools can integrate with continuous integration (CI) pipelines to prevent merging faulty code, reducing the risk of project delays and costly errors.

Skill Improvement:

• Code reviews allow developers to learn from their peers and improve their coding skills.
• With code review tools, less experienced developers can benefit from the feedback of more experienced team members, accelerating their growth.

Code Review Metrics and Analytics:

• Code review tools often provide valuable metrics and analytics, such as review completion times, code churn, and reviewer performance.
• These metrics can be used to assess the efficiency of the code review process and identify improvement areas.

Also Read: Top 10 Tips for Using Code Editors to Boost Productivity.

Types of Code Review Tools 

A. static analysis tools:.

Definition and Purpose: Static Analysis Tools are code review tools that analyze source code without executing it. Their primary purpose is to identify potential issues and vulnerabilities in the codebase before runtime. These tools ensure that code adheres to coding standards and best practices by examining the code’s structure, syntax, and potential security flaws.

Examples of Popular Static Analysis Tools:

• PMD : PMD is a Java-based static analysis tool that identifies common coding flaws, such as unused variables, code complexity, and code duplication.
• ESLint : ESLint is a static analysis tool for JavaScript that helps identify and fix coding style issues.
• SonarQube : SonarQube is a comprehensive code quality and security analysis tool that supports multiple programming languages.

B. Code Review Platforms:

Explanation and Functionality: Code Review Platforms are dedicated tools or platforms that facilitate the entire code review process, from creating code review requests to providing collaboration features for reviewers. They streamline the code review workflow, making it easier for teams to assess and improve code quality.

Highlighting Well-Known Code Review Platforms:

• GitHub : GitHub is a widely used platform that offers built-in code review features, including pull requests, code commenting, and integration with continuous integration tools.
• GitLab is an integrated DevOps platform that provides code review capabilities, version control, and CI/CD functionalities.
• Bitbucket : Bitbucket, developed by Atlassian, offers code review tools alongside Git and Mercurial version control systems.

C. Version Control System Integration:

How Version Control Systems Facilitate Code Reviews: Version Control Systems (VCS) are essential for code review because they enable developers to track changes, collaborate on code, and maintain version history. They facilitate code reviews by providing a structured environment for code changes to be proposed, discussed, and merged into the codebase.

Examples of VCS with Built-In Code Review Features:

• Git : Git, a distributed version control system , is commonly used for code review through features like branching, pull requests, and code diffing.
• Mercurial : Mercurial offers code review functionality similar to Git, with features like changesets and code comparison tools.

Perforce : Perforce is a version control system that supports code review through workflows like shelving and code review assignments.

Code Review Tool Case Studies

A. Real-World Examples of Organizations Using Code Review Tools Effectively:

Google (Using Gerrit):

• Google employs Gerrit for its code review process, which allows developers to review, comment, and approve code changes efficiently.
• Gerrit’s access controls and fine-grained permissions help Google maintain code quality and security.
• Code reviews in Google have become more structured, decreasing post-release bugs and improving code maintainability.

Facebook (Using Phabricator):

• Facebook developed Phabricator, an open-source code review tool, to support its extensive codebase.
• Phabricator enables Facebook’s large development teams to collaborate seamlessly, ensuring code consistency and reliability.
• The tool’s integration with other development tools streamlines the workflow, saving time and reducing bottlenecks.

Netflix (Using GitHub):

• Netflix leverages GitHub for code review, benefiting from its extensive features and integrations.
• Code reviews at Netflix are integral to their development process, ensuring high-quality code and timely releases.
• GitHub’s collaboration features enable cross-functional teams to collaborate effectively, promoting innovation and rapid development.

B. The Impact of Code Review Tools on Their Development Processes:

Enhanced Code Quality:

• In each of these organizations, code review tools have contributed to improved code quality by catching bugs, identifying potential security vulnerabilities, and enforcing coding standards.
• Developers receive feedback and suggestions from their peers, leading to cleaner and more maintainable code.

Accelerated Development Cycles:

• Code review tools streamline the review process, reducing the time required for approval and merging code changes.
• Faster code reviews mean quicker development cycles, enabling organizations to relieve new features and updates more frequently.

Collaboration and Knowledge Sharing:

• These tools promote collaboration among development teams, allowing for the sharing of knowledge and best practices.
• Developers learn from each other through code reviews, leading to skill improvement and a more cohesive development community.

Error Reduction and Improved Security:

• Code review tools help organizations identify and rectify issues early in development, reducing the likelihood of post-release bugs and security vulnerabilities.
• By catching problems before they reach production, these organizations maintain a more robust and secure software ecosystem.

Also Read: How to Use Debugging Tools to Improve Your Code?

Tips for Getting Started with Code Review Tools

A. Steps to Implement Code Review Tools in Your Development Workflow:

Assess Your Team’s Needs : Begin by understanding your team’s specific requirements for code review tools. Identify the programming languages, version control systems, and platforms you use. Help you choose an agency that aligns with your development stack.

• Select the Right Tool : Research a code review tool that suits your team’s needs and preferences. Popular options include GitHub, GitLab, Bitbucket, and various code review-specific agencies like Review Board and Crucible.
• Install and Configure the Tool : Follow the installation instructions for your chosen code review tool. Ensure it integrates seamlessly with your existing development environment, version control system, and issue tracking system.
• Define Code Review Guidelines : Establish clear and concise code review guidelines tailored to your project. These guidelines should include coding standards, best practices, and expectations for reviewers and authors.
• Training and Onboarding : Train your team on how to use the code review tool effectively. Provide guidelines on creating and responding to code review requests, setting up notifications, and using the tool’s features.
• Integrate with CI/CD Pipelines : Integrate the code review tool with your Continuous Integration/Continuous Deployment (CI/CD) pipelines and ensure that code reviews are integral to your development workflow, with automated checks triggering thoughts upon code submission.
• Start with Smaller Changes : Initially, encourage team members to start with more minor code changes, ease the learning curve, and ensure smoother adoption of the code review process.
• Monitor and Adjust : Continuously monitor the usage of the code review tool and gather feedback from your team. Make necessary adjustments to your guidelines and workflows to improve efficiency and effectiveness.

B. Overcoming Common Challenges When Introducing Code Review Tools:

• Resistance to Change : Some team members may resist adopting code review tools due to unfamiliarity or fear of increased workload. Address this challenge by highlighting the long-term benefits, such as improved code quality and knowledge sharing.
• Lack of Consistency : Ensure your code review guidelines are consistently applied across all code submissions. Implement automated checks to enforce coding standards and identify common issues, reducing the burden on reviewers.
• Review Backlog : As you introduce code review tools, a backlog of existing code may need to be reviewed. Prioritize and schedule these reviews to gradually catch up while maintaining current development efforts.
• Balancing Speed and Quality : Striking the right balance between rapid development and thorough code reviews can be challenging. Encourage quick turnaround times for reviews while maintaining the quality standards set in your guidelines.
• Effective Feedback : Teach reviewers how to provide constructive feedback that helps developers improve their code. Encourage a culture of feedback and collaboration, not criticism.
• Tool Integration : Ensure the code review tool integrates seamlessly with your development tools, such as version control and issue tracking systems. Compatibility issues can hinder adoption.
• Monitoring and Metrics : Implement metrics and key performance indicators (KPIs) to track the impact of code review tools on your development process. Use data to identify areas for improvement and celebrate successes.

By following these steps and addressing common challenges, you can successfully implement code review tools in your development workflow, leading to higher code quality and more efficient collaboration within your development team.

In conclusion, mastering code review tools is essential for any development team striving for excellence. These tools streamline the review process and ensure code quality, collaboration, and knowledge sharing. 

With best practices such as setting clear objectives, providing constructive feedback, and maintaining a positive and respectful environment, teams can harness the full potential of code review tools to produce high-quality code that drives innovation and efficiency. 

Remember, the benefits of code review extend far beyond mere error detection. They encompass knowledge sharing, mentorship, and cultivating a culture of quality within your development team. 

By integrating code review tools effectively into your workflow and embracing the best practices outlined here, you can enhance your codebase, accelerate development cycles, and ultimately deliver software of the highest caliber . So, as you embark on your journey of utilizing code review tools, keep in mind that optimizing your development process starts with optimizing your code review practices.

Tags: Code review , coding , Product Engineering , top code review tools , what is code review?

More Articles on this Topic

" rel="bookmark" title="How to Choose the Right Integration Platform for Your Needs?">How to Choose the Right Integration Platform for..

" rel="bookmark" title="Predictive Analytics for Data-Driven Product Development">Predictive Analytics for Data-Driven Product Development

" rel="bookmark" title="Kubernetes Storage: Options and Best Practices">Kubernetes Storage: Options and Best Practices

" rel="bookmark" title="Managing Infrastructure with Terraform and Other IaC Tools">Managing Infrastructure with Terraform and Other IaC Tools

" rel="bookmark" title="Integrating Containers with Security Tools like SELinux and AppArmor">Integrating Containers with Security Tools like SELinux and..

• Announcements
• Digital Strategy
• Enterprise Mobility
• Financial Services
• Hospitality
• Internet of Things

Recent Posts

• How to Choose the Right Integration Platform for Your Needs
• Predictive Analytics for Data-Driven Product Development
• Kubernetes Storage: Options and Best Practices
• Managing Infrastructure with Terraform and Other IaC Tools
• Integrating Containers with Security Tools like SELinux and AppArmor
• Best Practices for Designing and Maintaining Software Architecture Documentation
• Automated Testing and Deployment Strategies
• Understanding Database Consistency and Eventual Consistency
• What are the Benefits of Product-led Growth, and How can it be Implemented?
• Database Migration and Version Control: The Ultimate Guide for Beginners

Company Name

Work Email*

Phone Number

How can we help you?

First Name*

Description*

[anr_nocaptcha g-recaptcha-response]

We value your privacy. We don’t share your details with any third party

COMPANY EMAIL*

Create new digital lines of revenue and drive great retention and customer experience!

Find out how, from our tech experts.

Organization Name

Privacy Overview

Better hiring with code review assignments

Evaluating the software engineering skills of job candidates often involves live coding sessions or take-home coding assignments, but both methods have major limitations.

Live coding sessions don't reflect realistic work conditions. They only allow for the evaluation of relatively short code snippets and are susceptible to candidates’ emotional responses during the interview. Take-home assignments, on the other hand, are substantially time-consuming for both candidates and interviewers, slowing down the interview process, and leading to candidates abandoning the application process altogether.

Instead, a better approach might be code review assignments, which are less commonly used. However, code review assignments are perfect for evaluating candidates’ performance in a realistic work environment, saving time for both parties and allowing for deeper and more objective evaluation.

You will leave this talk with knowledge on:  

• What code review assignments are 
• How to implement code review assignments into your hiring process
• Insights on how code review assignments are able to evaluate candidates

Related content

Think faster, talk smarter: matt abrahams in conversation.

Practically engaging with AI

CONTENT SPONSORED by SPLIT

Fostering a culture of experimentation in your engineering teams

CONTENT SPONSORED by JELLYFISH

Strategies for leaders when planning the annual roadmap

CONTENT SPONSORED by DOIT

Designing the best cloud architecture for your org

CONTENT SPONSORED by KARAT

Everything you need to know about hiring engineers in 2023

Facilitating Software Architecture: Andrew Harmel-Law in conversation

How do you effectively manage senior ICs?

CONTENT SPONSORED by DESCOPE

Tackling the build vs buy conundrum

Learning to focus on the impactful tasks, instead of every ask!

How to better understand business context as a StaffPlus engineer

How to be right when it counts: 7 tools for making better decisions: Sho Soboyejo in conversation

Finding your path to vision and mission

Don’t let metrics be a distraction

The Three C’s of Company-Wide Collaboration

When the movie isn’t like the book: Failure modes in strategic alignment

Creating alignment across a project lifecycle

How to navigate uncertainty as a senior individual contributor

Leading Snowflakes: Oren Ellenbogen in conversation

CONTENT SPONSORED by SPLUNK

How to gain the right visibility across your teams as a busy manager

Daring to be different: Stories and tips from a woman leader in tech: Raji Rajagopalan in conversation

De-coding the technical interview process: Emma Bostian in conversation

CONTENT SPONSORED by ADOBE

How to succeed as a frontend developer today

Exploring A/B Testing: Leemay Nassery in conversation

Remote Engineering Management: Alexandra Sunderland in conversation

What DevOps teams need to know in 2023

Leveling-up your leadership team

How Observability Can Boost Engineering Productivity

The business value of an engineering year.

Evolving your core management skills: From IC to VP

CONTENT SPONSORED by CODESIGNAL

Team design is system design

Using principles of observability to drive your professional growth

CONTENT SPONSORED by SAUCE LABS

Why full SDLC testing matter in your software development lifecycle

Realizing a technical strategy during turbulent times.

CONTENT SPONSORED by SWIMM

Engineering your feedback: A (quick) guide to success

How do you build a great team culture? LMGTFY

What does an effective cloud strategy look like?

Common mistakes new staff engineers make… and how to avoid them

Corpse party bugs

Being a principal engineer. The world is your oyster.

Plug in to leaddev.

Want to get regular updates on all things LeadDev? Sign up to our mailing list

To find out more about how your data is handled check out our Data Promise

Python code review checklist

As developers, we are all too familiar with code reviews. Having another pair of eyes take a look at our code can be wonderful; it shows us so many aspects of our code we would not have noticed otherwise. A code review can be informative, and it can be educational. I can confidently attribute most of what I know about good programming practices to code reviews .

The amount of learning a reviewee takes away from a code review depends on how well the review is performed. It thus falls on the reviewer to make their review count by packing the most lessons into the review as possible.

This is a guide on some vital aspects of the code you should be checking in your reviews, the expectations you should have from those checks, and some ideas on how tooling (such as linters, formatters, and test suites) can help streamline the process.

Code review checklist

We have formulated this guide in the form of a checklist. It lists a set of questions that you need to ask about the code. If the answer to any of them is not a 'yes', you should leave a remark on the PR.

Do note that this list is only meant to serve as a guideline. Your codebase, like every codebase, has its own specific set of needs, so feel free to build upon this guide and override pieces of it that do not fit well for your use-cases.

The first and foremost thing to check during a review is how closely the PR adheres to basic etiquettes. Good PRs are composed of bite-sized changes and solve a single well-defined problem. They should be focused and purposefully narrow to have as few merge conflicts as possible. Put simply, a good PR facilitates being reviewed.

For large-scale swooping changes, make a separate target branch and then make small incremental PRs to that target, finally merging the target with the main branch. Making one humongous PR makes it harder to review, and if it goes stale, many merge conflicts may pop up.

When reviewing PRs from new developers, I also make it a point to ensure their commit messages are well-written .

• Is the PR atomic?
• Does the PR follow the single concern principle?
• Are the commit messages well-written?

Enforce a commit message format in the team. For example, you could try gitmoji wherein you use emoji in commit messages. For example, bugfixes should start with [FIX] , or the 🐛 emoji and new features should start with [FEAT] or the ✨ emoji. This makes the intention of the commit very clear.

Functionality and syntax

The next thing to check is whether the PR is effective in that it works. Code changed by the PR should work as expected. A bug fix should solve the bug it was supposed to fix. A feature should provide the additional functionality that was required without breaking something else.

An important thing to keep in mind is that any new feature added by a PR is justified. A simple way to ensure this is to accept only the PRs that are associated with an already triaged issue. This practice minimizes feature-creep .

• Does the PR work?
• Does the new feature add value or is it a sign of feature-creep?
• Does the PR add test-cases for the modified code?

Having comprehensive tests in the code makes it easier to check that new functionality works and harder for PRs to break existing stuff. The portion of the code covered should never go down in a PR. Any new code should come with complete coverage in unit/functional tests. Python comes with a robust unit testing framework built into the language itself. You should make use of it in your codebase.

Once we've established that the code works, the next step is to check how well it integrates with the existing codebase. One key thing to inspect in this regard is duplication. Often, code added to a repo provides functionality that might already be a part of the code in a different location or something provided by the frameworks or Pip packages in use. This knowledge is something that one can only gain by experience. As a senior, it becomes your duty to point such duplication out to new developers contributing to your repo.

Attention to paradigms is also essential. Many projects have pre-adopted design paradigms such as microservices, mono repo, or cloud-nativity. Any incoming code should be in line with these paradigms.

• Is the code properly planned and designed?
• Will the code work well with the existing code and not increase duplication?
• Is the code well organised in terms of placement of components?

Patterns and idioms

Python is a very mature language. It is driven based on certain philosophies, described as the Zen of Python . Those philosophies have given birth to many conventions and idioms that new code is expected to follow.

The difference between idiomatic and non-idiomatic code is very subtle, and in most cases, can only be intuitively gauged. As with all things honed by experience, intuition must be transferred from experienced people, like yourself, to newbies like your reviewees.

• Does the code keep with the idioms and code patterns of the language?
• Does the code make use of the language features and standard libraries?

Most linters, popularly PyLint , can help you identify deviations from the style guides and, in most cases, even automatically fix them. Linters work incredibly fast and can make corrections to the code in real-time, making them a valuable addition to your toolchain.

Readability

Python is widely regarded as a very readable language. Python's simplicity of syntax and reduced usage of punctuation contribute heavily to its readability. It only makes sense for code written in the language to be readable as well.

• Is the code clear and concise?
• Does it comply with PEP-8?
• Are all language and project conventions followed?
• Are identifiers given meaningful and style guide-compliant names?

A good code formatter like Black can help a lot in formatting the code for consistency and readability. Black also offers minimal customization, which is good because it eliminates all forms of bikeshedding.

We've previously talked about integrating Black into your CI pipeline can work wonders during a code review.

Documentation and maintainability

The next thing to check is the maintainability of the code. Any code added or changed by the PR should be written to facilitate someone other than the original author to maintain it.

It should preferably be self-documenting, which means written in a way that anyone reading the code may be able to understand what it does. This is one of the hallmarks of good Python code. If the code has to be complex by design, it should be amply documented. In an ideal world, all classes and functions would have Python docstrings, complete with examples.

• Is the code self-documenting or well-documented?
• Is the code free of obfuscation and unnecessary complexity?
• Is the control flow and component relationship clear to understand?

Sphinx is a documentation generator that exports beautiful documentation from Python docstrings. The exported documentation can then be uploaded to ReadTheDocs , a popular doc-hosting tool. Sphinx is one of the main reasons why I absolutely love writing documentation.

Ensuring that the application remains secure is critical. The next thing to check is if the PR maintains or improves the security of the project. You need to ensure that the changes do not increase the attack surface area or expose vulnerabilities. If the PR adds new dependencies, they could potentially be unsafe, in which case you might need to check the version for known exploits and update the dependencies, if necessary.

• Is the code free of implementation bugs that could be exploited?
• Have all the new dependencies been audited for vulnerabilities?

One of the renowned security analyzers for Python is Bandit . Also, if you use GitHub for hosting code, you should absolutely read this guide about setting up vulnerability detection and Dependabot for your codebase.

Once you set up vulnerability detection on GitHub, you'll get notifications like this...

... with details about the vulnerability and PRs in your repositories that you can merge to patch them.

We've also recently talked about common security pitfalls in Python development and how you can secure your projects against them.

Performance, reliability and scalability

The final things to check are the performance and reliability of the code at scale. While these are undoubtedly key metrics, I put them on the bottom of the checklist because I believe well-planned, well-designed, and well-written code generally works well too.

• Is the code optimised for in terms of time and space complexity?
• Does it scale as per the need?
• Does it have instrumentation like reporting for metrics and alerting for failures?

An excellent way to have add some reliability to Python is to use type hinting and static type checking that can provide hints into possible errors before runtime. Python new native support for type-hints is primarily inspired by the Mypy syntax, which can be incrementally adopted in existing projects.

DeepSource is an automated code review tool that manages the end-to-end code scanning process and automatically makes pull requests with fixes whenever new commits are pushed or new pull requests.

Setting up DeepSource for Python is a quick, easy, no-fuss process. Just add a .deepsource.toml file in the root of the repo, and immediately DeepSource will pick it up for scanning. The scan will find scope for improvements across your codebase, make those improvements, and open pull requests for the changes it finds. I was blown away by the simplicity of the setup and the efficacy of their self-built code engine.

🙋‍♂️ Trivia: Did you know that DeepSource holds the distinction of being on OWASP’s curated list of source code analysis tools for Python?

The ideal code review

Returning to my point about code reviews being educational and informative, I would also like to add that code reviews are time-consuming and resource-intensive. While each review takes time, the more in-depth and comprehensive ones consume even more.

So each review must be as productive as possible. This is where all the tooling and automation efforts should be directed. By automating whatever can be automated, which is generally the mundane parts of the review, such as code style and formatting, we can allow devs to focus on the important stuff like architecture, design, and scalability.

I'll leave you with a profound thought my mentor shared with me.

A good code review not only improves the code but the coder as well.

More from DeepSource

Get started with deepsource.

DeepSource is free forever for small teams and open-source projects. Start analyzing your code in less than 2 minutes.

Read product updates, company announcements, how we build DeepSource, what we think about good code, and more.

Teaching Code Review to University Students

Anyone who has worked in software development professionally knows about code review: The idea that developers review the code of other developers to spot errors, propose suggestions for improvement and to ensure that knowledge is shared within the development team.

Code reviews are effective, common in the industry, and at the same time really hard to do. One would think that university would teach you a best practice from the industry — but that is not the case for code reviews.

There are multiple reasons why lecturers in university do not teach students how to do code review:

• They don’t know how to teach it
• They don’t know how to assess it
• They come from an academic background where code review rarely happens

I am a software developer and have done some code reviewing myself. I am also a university lecturer in computer science . To be honest, reviewing code made by others is one of the hardest things I know.

I will outline an approach to teaching code reviews with peer feedback. Beyond teaching students to do code reviews, peer feedback has the added benefits of teaching students about the subject matter and how to think critically.

How to teach code reviews with peer feedback

In essence, the way I propose that you can teach the skill of code review is the following setup:

• Let students submit their code for review prior to the actual submission deadline.
• Ask each student to review code from 2–3 other students using a feedback rubric to help them focus their review.
• When students receive their reviews, ask them to give feedback to the reviewer on the usefulness of the review.
• Finally ask the students to use their received feedback to improve their code for the final submission.

Let students resubmit the work

Receiving feedback is only useful if you can use that feedback for something. Providing a review to someone that can’t use it will still teach both the reviewer and the submitter something valuable. However, the effect is much clearer if students get a chance to improve their submissions based on the feedback.

One of the challenges this brings, is that it takes more time since students will need time to do their reviews and make improvements to their work after submitting the first version of their work. There is really no good solution to this problem.

Another challenge is the potential for increased plagiarism. If all students are working on very similar tasks (like solving a simple programming exercise) then students will (being somewhat rational as they are) steal good ideas and solutions from the work they review. When students are working on more open-ended and different projects, this is less of a problem though.

Let them give feedback on the review

In order to incentivize students to make great reviews, I propose letting them give feedback on the reviews they receive. One way this feedback could be given is by asking the receiver of a review to answer the following survey for each review:

• Constructivity: Is the review helpful and explain how to improve your code? (Possible answers: No needs more work / Somewhat / Yes it was great).
• Specificity: Does the review point to specific things in your code? (Possible answers: No needs more work / Somewhat / Yes it was great).
• Justification: Does the review provide explanations and give reasons and arguments? (Possible answers: No needs more work / Somewhat / Yes it was great).
• Kindness: Is the review kind and uses friendly language? (Possible answers: It was too harsh / It was neutral or friendly).
• Open feedback: Do you have any comments to the reviewer?

Using the answers to the first four questions above, it is even possible to compute a review score — for example by treating “No needs more work” as 0% and “Yes it was great” as 100% and then averaging the scores together). This review score can either be used as a guide for the teacher on which students need more help with their reviews, or (as in my course) even be a part of the final grade in the course.

Some research actually suggests that this approach of giving marks for providing helpful feedback is a great way to encourage students to assess work accurately.

Limit the size and scope of the review task

Reading other people’s code is hard. To make code review effective, refrain from asking students to review too much code at the same time. This resource claims that “… the single best piece of advice we can give is to review between 100 and 300 lines of code at a time and spend 30–60 minutes to review it.”.

Another way to help students provide good reviews is to help them focus their feedback on specific things. Generally, you should not use code review to check if the code is working correctly (that is what tests and a compiler is for). Ask students to focus on style, comments and documentation, modularity, use of testing, error handling, security, algorithms etc. These are things that humans are (still) somewhat useful for checking.

One way to help students focus is by using a feedback rubric. You can present it in the form of a small survey, for example like this:

• Documentation: Is the code properly documented and commented? (Possible answers: It needs more work / Somewhat / Yes it is great). Where should the documentation be better?
• Error handling: Does the code handle errors properly? (Possible answers: It needs more work / Somewhat / Yes it is great). Where should the error-handling be better?
• Suggestions: Provide two suggestions for the author on how to improve the code.

Allow students to object and discuss the reviews

Quality of code is rarely black and white. There will be disagreements, different correct solutions and personal preferences. After students have submitted their feedback on the reviews they received, let them continue the discussion both with each other and with you as the teacher. Having a discussion around the code review is a great way for students to learn from each other and learn to communicate about a technical subject.

If the review process has any influence on the grades that students are getting, it is very important that students have a chance to object and get a teacher evaluation of their work if they disagree. Similarly if the quality of a review contributes to the grade, then they should also be able to object to the feedback on their reviews.

Teaching code review with Eduflow

Beyond teaching university, I am also co-founder of Eduflow, a product for instructors to run engaging online learning experiences (for example peer review). The way we have designed Eduflow is very much in line with the pedagogical design mentioned above.

Students can submit their work either as code-files, iPython notebooks or just as links to Github (this all happens in a submission activity). It is possible to run the entire process of peer review in a double-blind anonymous setting (you can use our peer review flow for this).

Eduflow automatically takes care of assigning reviewers to code in a smart way and allows you to set up a feedback rubric easily. You also get the feedback-on-feedback (through the feedback reflection activity) easily. Finally, instructors get a quick and thorough overview over the entire process so they can quickly get an understanding of what students are excellent at and where they need to focus their teaching.

Edit: As with all good ideas, I am of course not the first person to think of this. In a series of blog posts and finally a thesis project , Mike Conley (who is now a software developer at Mozilla) goes through an experiment on the effectiveness of teaching students to do code reviews through peer assessment. In his work he comes to the conclusion that students learn from reviewing code, that students are able to fairly accurately assess code quality, but also that students generally don’t like to grade the work of their peers (they don’t mind reviewing it — it is just the grading). Part of his setup is letting teaching assistants grade the work first, and then giving students credit based on how close their marks are to those given by the TA — which according to the research I mentioned above might actually have the opposite effect!

Quick navigation

Talk to us the way you prefer, request a demo, get a quote, email sales.

Types of code reviews: Improve performance, velocity, and quality

February 28, 2024

Ana was exhausted. Her team had just resolved a major incident caused by a bug that had made it into production, wreaking havoc for their customers. The feature involved a complex revamp of their recommendations engine—one of the biggest changes they had ever undertaken.

Dealing with hundreds of lines of intricate new logic, it was clear that reviewers had focused only on the parts they understood, assuming the rest was fine.

As the lead developer, she took responsibility for the failure in their review process and recognized that in their rush to complete the key feature, some code reviews had been no more than a rubber stamp.

Why invest in code review efficiency?

On the surface, code reviews may seem like an unnecessary drag on developer velocity. However, data reveals that structured reviews can improve quality and productivity—when implemented effectively.

Big ideas developed in a vacuum are doomed from the start. Feedback is an essential tool for building and growing a successful company. — Jay Samit , Independent Vice Chairman, Deloitte Digital

According to Steve McConnell’s book Code Complete , code inspections discover over 60% of defects compared to 25-45% for standard testing. Here are a few more stats from the book:

Code reviews cut errors by over 80% at Aetna, enabling a 20% decrease in dev staffing.

AT&T saw a 14% productivity boost and a 90% decrease in defects after introducing code reviews.

63% of new devs were able to learn and use Git in one semester, indicating change process adoption viability.

At Graphite, we found that teams following PR size best practices have PRs hovering around the 50-line average and ship 40% more code than a similar team doing 200+ line PRs. Smaller PRs also make writing proper unit tests for each module easier and make reverting regressions much easier.

This data demonstrates that thoughtfully designed reviews can isolate objective changes and provide a model for increasing release stability. Consider consistent, structured code reviews as one of the main pillars of the development process.

When done right, code reviews improve product quality and team productivity—but the review model must adhere to modern code review processes.

What review models best balance robustness and speed? Let's explore the primary types of reviews and their tradeoffs.

Types of code reviews to improve performance

Let’s pick back up with Ana and the team.

It was time for them to evaluate their code review process to prevent incidents like this moving forward. Ana knew proper code reviews were necessary to improve performance, velocity, and quality.

“If an egg is broken by outside force, life ends. If broken by inside force, life begins. Great things always begin from inside.” – Jim Kwik, Learning Expert.

Diverse developers and teams employ various types of code reviews. Let's explore the most popular methods and evaluate their compatibility with Ana and her team.

1. Formal code review

A formal code review is a structured, thorough process involving multiple phases and participants that helps examine code for defects. Originating from Michael Fagan's work in the 1970s , this method emphasizes defect detection rather than correction or improvement.

The process typically develops in several stages:

Overview meeting

Preparation

Inspection meeting

Causal analysis

The plan is to identify a wide array of defects—ranging from 60 to 90 percent.

Each participant plays a specific role, including the moderator, program designer (or architect), developer (or coder), and tester, contributing to a comprehensive and detailed review.

Inspections are time-boxed to maintain efficiency, with two-hour sessions being optimal to prevent a decrease in error detection effectiveness.

While formal reviews are highly effective in finding defects, they can be time-consuming and require significant preparation and participation effort.

Typically, formal code reviews are not very scalable. Especially when you know systems do get complicated over time.

As a program evolves and acquires more features, it becomes complicated, with subtle dependencies between its components. Over time, complexity accumulates, and it becomes harder and harder for programmers to keep all of the relevant factors in their minds as they modify the system. This slows down development and leads to bugs, which slow development even more and add to its cost. The larger the program, and the more people that work on it, the more difficult it is to manage complexity. — From the book A Philosophy of Software Design .

Ana considered whether formal reviews could have prevented the issue they experienced. The structured preparation and time-boxed inspection meeting would certainly have encouraged a more thorough checking of the entire change set.

However, she was concerned that the formality and time required would not work well with her team's Agile processes. The cost of disrupting development to prepare and participate in lengthy review meetings outweighs any benefits.

2. Lightweight code review

Lightweight code review offers a more flexible and less resource-intensive approach than formal reviews. They generally include several methods, such as pair programming, over-the-shoulder, async reviews, and tool-assisted review.

These methods share a common goal of speeding up the feedback loops and integrating easily into the development workflow without the extensive setup of formal inspections.

Pair programming

Two developers work simultaneously on the same piece of code, effectively conducting a continuous review process. This approach fosters mutual motivation and maintains focus, especially among developers of similar experience levels.

The appeal of pair programming for certain tasks may be clear, but across a whole project, it would be impractical. Two developers on Ana’s team would rarely be working in the same area of the codebase. This approach may be more practical for larger businesses with an established product that needs to be maintained.

Over-the-shoulder code review

These reviews occur in real-time, with the reviewer joining the coder at their workstation to go through the code together. This method is most useful when the reviewer needs more familiarity with the task's objectives or anticipates substantial code improvements.

“Over the shoulder is often the developer explaining their decisions in the code, instead of the reviewer trying to reverse-engineer it, independently. It's just faster and has less resistance -- not necessarily better. The problem with remote live reviews is that in a remote environment, it's harder to tell if someone is free or if they are doing their deep work. Either the developer has to wait for the review to be done asynchronously before the merge... or ping someone to review their code through a screenshare and take away their attention.” — Hacker News user, aman-pro

However, it can lead to forced context switching, negatively impacting the reviewer's productivity and the team's overall efficiency.

While synchronous reviews by a reviewer sitting at a workstation could be valuable, Ana's team was fully remote across multiple time zones. Real-time over-the-shoulder review would be almost impossible to coordinate.

Asynchronous code review

This type of code review allows the coder and reviewer to operate independently, with the reviewer examining the code and providing feedback at their convenience. It minimizes the disruption associated with synchronous reviews but can lead to extended review cycles spread over several days. Some teams prioritize reviews at specific times, such as after breaks, to mitigate delays and maintain a reasonable review turnaround.

Asynchronous reviews may be a good fit, allowing Ana's globally distributed team members to inspect code without forcing real-time alignment of schedules. However, she worried that long feedback delays could still be an issue without some way to focus reviews.

Tool-assisted code review

This strategy uses specialized code review tools to streamline and enhance the review process. These tools facilitate simplified workflows for submitting changes, requesting reviews, annotating code, tracking issues, and approving/rejecting alterations.

Modern code review platforms aim to assist teams in performing effective reviews without frustration. The most capable tools like Graphite , GitHub, GitLab, and Phabricator build lightweight code review workflows on top of existing systems.

Automation can streamline rote tasks like assignments, notifications, metrics gathering, policy compliance tracking, and more. However, restrictive automation that strictly dictates practices can hinder productivity, especially when developers have a preferred workflow. The most effective systems strike a balance—providing helpful guidance while keeping humans firmly in the loop.

On the other hand, Tool assistance can incorporate team standards and best practices directly into the existing flow of work. Checklists, templates, and visibility help streamline lightweight reviews without excessive processes and SOPs.

Ana's globally distributed team members often face challenges aligning schedules for synchronous reviews. While tool-assisted code review offers benefits such as consistency, reduced manual effort, and customizable workflows, Ana worries that relying solely on tools could further complicate the issue. On the other hand, automated processes might unintentionally limit their ability to work together without careful adjustment to fit their specific needs. So, while code review tools are invaluable for efficiency, Ana recognizes the importance of balancing their benefits with the need for flexibility in her team's workflow.

3. Pull requests for change-based code review

Pull requests have become a standard in open-source and commercial development for improved code review. You can use pull requests for pair programming, formal code reviews, and most other code reviews—making it a flexible strategy, which is why most companies stick to pull request-based code reviews.

This method uses a version control system (VCS) like Git to submit code changes for review before merging, supporting collaboration and iterative feedback through comments and approvals.

Many development teams adopt this method due to its streamlined integration into daily workflows. If your team follows this method, you may also want to ensure they adhere to the pull request best practices for improved efficiency.

Ana could see how using pull requests as the vehicle for their code reviews could address some of the issues that led to their recent incident:

PRs connected to tickets make the scope for review more manageable.

The PR approval process acts as a speed bump, preventing changes from being merged without proper inspection.

Comments and version histories support discussion and iterative improvement of the changes.

Such change-based reviews would align well with Ana's team's Agile approach of working in fixed-length sprints and tracking progress via user stories and tickets.

However, one notable challenge with regular pull request workflows is that PRs often tend to become sizable, leading to review delays.

Large PRs could wait for a review for days—and, according to surveys, sometimes even years .

Thoroughly examining and validating thousands of lines of code across multiple files, while comprehending the PR's purpose, can be overwhelming for reviewers.

“I’ve found that pull request size solves a lot of the issues that people have with code reviews and quality. When people see a very large pull request, there is a tendency to skim and then slap on an Approval. Keeping pull requests small typically leads to a more thorough review because it’s much easier to parse the changes and build a mental model. This usually leads to better feedback. This also helps prevent less experienced devs from going crazy down the rabbit hole and making a huge code change. Small and steady is best, and fostering a culture where people are often asking each other questions and collaborating is key.” — Hacker News user, matthewwolfe

Large PRs may also lead to negligence, and reviewers may approve buggy code. In these situations, reviewers may do a quick skim instead of a detailed review—making it easier for bugs to go unnoticed and reducing the review process's thoroughness.

This issue is common enough that there is a large stock of memes floating around the web.

What’s the solution? 

Smaller, focused PRs enabled by stacked pull requests. Pull request stacking encourages a modular breakdown of massive changes into interconnected stacks of bite-sized PRs. This complements reviews by reducing complexity, making changes easier to validate without blocking progress.

Let’s understand them in more detail.

4. Stacked pull requests: An improved way to do pull request code reviews

Our analysis of over 1.5 million pull requests compared the number of files changed to the time those PRs took to merge. The data revealed clear patterns:

The fastest PRs changed the fewest files, with a median time-to-merge 3X higher for 5+ file changes.

Review complexity grows with more files, requiring elevated cognitive effort.

Git's per-file model means more files increase rebase conflicts .

Stacked pull requests involve breaking large feature changes down into a sequence of small, dependent pull requests that build on each other like a stack.

This forces changes to be structured into logical building blocks that are easy to review incrementally and without blocking progress. However, most code review tools are not built to support stacking. They approach code reviews traditionally, leaving much room for improvement.

That’s where Graphite comes in.

Benefits of stacked pull requests in code review

Graphite automates the Git branching and syncing required to maintain the relationships between stacked PRs.

However, the key benefit is the fundamental shift towards modular, layered changes that reduce complexity for authors and reviewers.

Stacked pull requests divide large code changes into smaller, interconnected pieces, simplify the review process, and enhance comprehensibility for those evaluating the code. Additionally, this method allows developers to maintain a swift pace of work without compromising accuracy. Let's explore the key advantages:

1. Massive changes get broken down

Reviewing a massive pull request that alters a vast amount of code is overwhelming, making it hard to keep track of the numerous interconnected changes. Dividing these changes into smaller, logically organized stacks clarifies and focuses each part. This approach enables code reviewers to easily understand small changes while maintaining an overview of the entire project.

For example, you could organize enhancements to a checkout page on a shopping website into separate, sequential stacks, such as:

Change the page layout.

Improve the order summary display.

Add upsells before the checkout button.

Integrate additional payment gateways.

Modify the order processing method.

Instead of a monster pull request, the stacks split it so experts can check more easily.

2. Connections between PRs are crystal clear

Figuring out hidden connections between changes is hard—especially when the code reviewers don’t have enough context. Stacked pull requests from within Graphite, visually lay out how each part fits together flat out.

Reviewers can instantly see relationships, and keeping stacks working as stuff shifts becomes super easy. These interdependent stacks make understanding relationships easier, thus making for a much more thorough code review.

3. Development does not get blocked on review

With stacked PRs, developers no longer have to wait for the review to be completed and merged before moving to the next feature or part of the feature.

They can start a new branch from the feature branch, write the new code, and submit that as another small, independent PR.

As the reviewers go through the PRs, they can suggest changes that the developer can now make, and the changes are automatically synced into the stacked PRs.

Because the approvals or feedback are faster than traditional methods, code flows through the pipelines much quicker, releasing new features more frequently.

"I've been using Graphite for a week and it's already saved me ~20 hours of work." — Forbes Lindesay, Senior Software Engineer, Mavenoid

Ana could immediately see how Graphite’s stacked PR approach could have prevented their recent incident:

Large, unstructured changes are transformed into manageable, incremental improvements.

Direct dependencies between changes are explicitly defined, making their relationships clear.

Each small PR allows for a thorough review with minimal effort, enhancing code quality.

While the stacked PR workflow would require a mindset shift, the investment would pay dividends in more efficient reviews and increased dev velocity in the future.

What’s the right code review process for your team?

Look, there is no "perfect" code review process. Each code review process has its place, depending on the project's requirements, team, and goals.

Modern Agile teams, especially distributed ones, should actively default to change-based reviews through pull requests. Pull requests streamline submitting code for inspection and support vital collaboration through comments and iterations. The key then becomes using automation to maximize the effectiveness of PR-based reviews.

This is where stacked pull requests shine.

Breaking large changes into modular building blocks reduces complexity, and reviewers can easily inspect changes without blocking overall progress.

Tools like Graphite take this to the next level by fully automating the Git workflow required to interlink stacked PR dependencies. The code review velocity is further boosted through auto-assignment, notifications, and metrics. Graphite's UI lowers the activation barrier so teams can easily shift to the new stacked PR workflows.

"With Graphite’s stacks, sync, and simplicity, engineers at The Browser Company can prototype, review, and ship features faster" — Andrew Monshizadeh, Software Engineer, The Browser Company (Arc Browser)

The bottom line is that centralized change review is necessary for any team serious about quality. Transition to stacked pull request workflows, improve code review accuracy, and get unblocked with Graphite today.

Sign up for free now and let your team experience the benefits of simpler, faster, and more effective code reviews from day one.

Related posts

Why large companies and fast-moving startups are banning merge commits

November 7, 2023

Accurate eng estimations: predicting and negotiating the future

January 24, 2024

Building trust as a software engineer

February 13, 2024

Give your PR workflow an upgrade today

Stack easier | Ship smaller | Review quicker

Using a Code Review Assessment to Assess Software Engineering Talent

Introduction

Building a practical interview process for software engineers is hard. It especially gets harder when you are trying to hire senior engineers. Senior engineers have to do a variety of tasks on the job from designing large-scale applications to mentoring junior engineers. When evaluating their technical skills, they can often get offended if you give them a screening quiz involving data structure and algorithm questions. How relevant is that to the job?

Enter the "Code Review Assessment" . This is a new interview type that is gaining popularity fast, especially among hiring and vetting senior engineers.

What is a Code Review Assessment?

In this assessment, candidates would receive a pull request (or merge request) and would be asked to perform a code review. Instead of asking the candidate to write code, the candidate will be required to read the source code and provide detailed comments on issues and areas of improvement. This type of assessment does not only measure technical skills, but important soft skills like communication, ability to give feedback, and attention to detail.

Benefits of Code Review Assessments?

Code review assessments have a variety of benefits - mainly that they can be completed in a short amount of time and provide a lot of signals about a candidate. In the interview process, you often want to optimize the "time-to-signal" ratio - how do you get the most amount of signal in the shortest amount of time? The code review assessment can help with that. Here are a few of their benefits.

Code Reviewing is Faster Than Writing Code

Most technical assessments take too much time. A code review assessment can be confined to less than one hour (on a call or through a take-home fashion) compared to other types of assessments that require candidates to write code.

Code Reviewing is a Relevant Skill

Unlike traditional data structure and algorithm assessments, providing feedback on code is a relevant on-the-job skill. Code review is a common responsibility for an engineer of all levels. It measures not only coding skills but also evaluates communication and other crucial soft skills.

It Fits Well in the Interview Process

The code review assessment can feed nicely into subsequent stages of the interview process. For example, following a code review assessment, you might ask candidates to write code to fix mistakes they identified. This makes the entire process seem more cohesive as opposed to a disjointed series of steps that interview processes often feel like.

Challenges of Code Review Assessments

Like all interview styles, there are some drawbacks to this interview format.

Cost of Implementation

A code review assessment can be difficult to create and maintain. Often times you want to make the assessment relevant, so you'll need to refresh it once in and while so the content stays relevant. Furthermore, if you want to offer candidates a choice of programming language, you'll need to create multiple versions of the same assessment in different languages. This can be hard to accommodate.

Difficulty in Maintaining Consistent Standards

Since this assessment involves assessing soft skills as well as hard skills, it can be difficult to maintain consistent standards when reviewing these assessments. You'll want to be sure that you create an effective scorecard to evaluate solutions.

Writing Code is not Evaluated

One of the biggest drawbacks of this assessment style is that you are not assessing how well candidates can write code. You would have to combine this assessment with another interview step if you'd want to see the candidate's proficiency in producing code.

Addressing these Challenges

In the rest of this post, we will share how to create a code review assessment that addresses some of these challenges. Firstly, we share methods on how you can create a consistent standard by sharing how we use feedback forms to evaluate solutions. Secondly, we share one example of a code review assessment to help you get started and reduce the cost of implementation. If you are looking for solutions to help you create and maintain code review assessments be sure to check out Hatchways .

Creating a Code Review Assessment

As mentioned in our creating a React assessment blog post , to create an assessment, you don't start with the assessment. Instead, you need to start by identifying the key skills you'd like to test. Here is a rough list of the steps required to create a code review assessment:

• Identify the evaluation criteria: The first step is to determine the key topic areas and skills you'd like to assess. You'd want to consider the role you are hiring, the seniority level you are targeting, and the type of day-to-day work they would be doing.
• Gather inspiration: The next step is to get some inspiration for the assessment. Your own code base can be one area of inspiration. Look at past pull requests you had in your organization and filter by those with lots of comments (you can use a query like `is:pr sort:comments-desc`). Alternatively, you can find inspiration from existing assessments online (like those on hatchways.io ) or the example we will provide below.
• Build the assessment: Based on your inspiration, it is time to create the assessment. The nice thing about a code review assessment is you don't need the code to be perfect!
• Develop the feedback form: Before sending this off to other candidates to complete, it is important to build a feedback form that you'll be using to evaluate the solution. Make sure to focus on the evaluation criteria you determined in Step 1. In this section, you'll want to create a code review checklist containing all the code review comments you'd expect a candidate to find. This feedback form can be iterated on as you test and iterate on the assessment. The quality of your assessment is based both on the assessment itself as well as the feedback form used to evaluate the solution.
• Test and iterate: Your first attempt at the assessment will not be perfect! No need to worry, it is important to track candidate experience and the signal you are receiving from the assessment to improve it over time. Again, you want to optimize the "time-to-signal" ratio.

An Example Code Review Assessment

In this section, we go over an example code review assessment. We will be sharing snippets of the assessment itself. However, if you want to see the full assessment, you do so here (you'll need a Hatchways account first to view this link).

The Challenge

In this assessment, candidates are asked to review the implementation of a cron job for a payroll management application. The cron job is supposed to run daily emails to notify administrators of each company that they must run payroll for a specific employee today. Here is a snippet of some of the code a candidate would have to review:

As you can see from the code, the employees are paid on different schedules (`bi-weekly`, `bi-monthly`, and `monthly`). The pull request also contains automated tests (where two tests are actually failing due to some bugs in the code).

The Feedback Form

As mentioned earlier, it is important to develop both a good assessment as well as a feedback form that will be used to evaluate the solution. In this section, we will share a glimpse of the feedback form we use to evaluate this assessment to help demonstrate how to create an effective feedback form.

Code Review Checklist

The first part of the feedback form is a checklist containing a specific list of items that we'd expect an engineer to comment on during the code review process of this assessment. Each line item in the checklist should be specific and you are just looking for the presence of comments in this part of the feedback form. Here are a couple of examples of checklist items (not specific to this assessment, as we wouldn't want to give candidates an edge on what we are looking for!):

Notice that the checklist has a subheader ("Comments on Code Quality"). This allows you to group the comments found into different categories that can match the next part of the feedback form.

The Scorecard

The next part of the feedback form is the scorecard, which allows our reviewer to give a rating on how the candidate performed in a variety of different categories. Some of these categories will correspond to the subheaders in the previous checklist question and some of these categories will be about the overall performance of the code review. When developing a scorecard, it is important to be explicit about what each category and level means.

In this assessment, we use the following categories in our scorecard:

• Comments on Code Quality
• Identifying Bugs and Suggesting Improvements
• Comments on Efficiency / Optimization
• Tone & Clarity of Feedback
• Comments on Product and User Experience

The Decision Question

The last question in the feedback form is the "Decision Question", which summarizes the reviewer's opinion on the candidate's submission. It is essentially the decision if the candidate should move forward in the interview or not.

It is important that this question is not answered based on just gut feeling. Instead, the other questions in the form should be used to determine this overall rating combined with the expectations for the candidate's seniority level. You will want to create a marking guidelines document that outlines what each level means for different seniority levels as shown in this blog post .

From our experience, code review assessments have received great feedback from candidates completing the assessment as well as interviewers using them in their interview process. The image below is from a Hatchways customer that used this specific code review assessment in their interview process.

We hope this post helped demonstrate some of the benefits of a code review assessment and provided you with an example so you know what is involved in creating one. If you are looking for some more examples, be sure to check out the Hatchways assessment catalogue.

Subscribe for more

Advertisement

Code reviews in open source projects : how do gender biases affect participation and outcomes?

• Published: 05 June 2023
• Volume 28 , article number  92 , ( 2023 )

Cite this article

• Sayma Sultana 1 ,
• Asif Kamal Turzo 1 &
• Amiangshu Bosu   ORCID: orcid.org/0000-0002-3178-6232 1  

514 Accesses

5 Altmetric

Explore all metrics

Contemporary software development organizations lack diversity, and the ratios of women in Free and Open-Source Software (FOSS) communities are even lower than the industry average. Although the results of recent studies hint at the existence of biases against women, it is unclear to what extent such biases influence the outcomes of various software development tasks.

This study conceptually replicates two recent studies by Terrell et al. and Bosu and Sultana that investigated gender biases in FOSS communities. We aim to identify whether the outcomes of or participation in code reviews (or pull requests) are influenced by the gender of a developer. In particular, we focus on two outcome aspects (i.e., code acceptance, and review interval) and one participation aspect (i.e., code review participation) of code review processes.

With this goal, this study includes a total of 1010 FOSS projects. Ten out of those projects use Gerrit-based code reviews. The remaining 1000 are randomly selected from the GHTorrent dataset based on a stratified sampling of projects fitting certain criteria. We divided GitHub projects into four groups based on the number of distinct contributors. We developed six regression models for each of the 14 datasets (i.e., 10 Gerrit based and 4 GitHub-based) to identify if code acceptance, review intervals, and code review participation differ based on the gender and gender-neutral profile of a developer.

Our results find significant gender biases during code acceptance among 13 out of the 14 datasets, with seven favoring men and the remaining six favoring women. We found significant differences between men and women in terms of code review intervals, with women encountering longer delays than men in three cases and the opposite in seven. Our results indicate reviewer selection as one of the most gender-biased aspects, with 12 out of 14 datasets exhibiting bias. A total of 11 out of the 14 cases show women having significantly lower code review participation than their men colleagues. Since most of the review assignments are based on invitations, this result suggests possible affinity biases among the developers. We also noticed a significantly higher likelihood of women using gender-neutral profiles. Supporting Terrell et al.’s claim, women with gender-neutral profiles had higher odds of code acceptance than men among three Gerrit-based projects. However, contradicting their results, we found significantly lower odds of code acceptance for women with gender-neutral profiles across all four GitHub project groups.

Conclusions

Though gender bias exists among many projects, the direction and amplitude of that bias vary based on project size, community, and culture. Similar bias mitigation strategies may not work across all communities, as the characteristics of biases and their underlying causes differ. As women are less likely to be invited for reviews, FOSS projects should take initiatives to ensure the equitable selection of women as reviewers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

An empirical comparison of ethnic and gender diversity of DevOps and non-DevOps contributions to open-source projects

Nimmi Rashinika Weeraddana, Xiaoyan Xu, … Meiyappan Nagappan

Women in Free/Libre/Open Source Software: The Situation in the 2010s

The impact of human factors on the participation decision of reviewers in modern code review

Shade Ruangwan, Patanamon Thongtanunam, … Kenichi Matsumoto

Data availability

Our data mining and analysis scripts and aggregated dataset are publicly available at https://doi.org/10.5281/zenodo.760853 . Due to privacy concerns, we do not make the full dataset, which is more than 1TB publicly available. The authors would be happy to share the dataset with researchers upon contact.

https://diversity.google/

5 \(\times \) 5 pixel sprites that are generated using a hash of the user’s ID.

https://www.healthline.com/health/different-genders

https://github.com/tue-mdse/genderComputer

https://developers.google.com/people

For our second sub-hypotheses (i.e, \(H1.2_a, H2.2_a\) , and \(H3.2_a\) ), we consider a project as supporting if either of the two gender groups indicates differences between members with and without GIPs.

https://gendermag.org/

Alba B (2018) To achieve gender equality, we must first tackle our unconscious biases. http://theconversation.com/to-achieve-gender-equality-we-must-first-tackle-our-unconscious-biases-92848

Augustine V, Hudepohl J, Marcinczak P, Snipes W (2017) Deploying software team analytics in a multinational organization. IEEE Softw 35(1):72–76

Article   Google Scholar  

Bacchelli A, Bird C (2013) Expectations, outcomes, and challenges of modern code review. In: 2013 35th International Conference on Software Engineering (ICSE), IEEE, pp 712–721

Barnett M, Bird C, Brunet J, Lahiri SK (2015) Helping developers help themselves: Automatic decomposition of code review changesets. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol 1. IEEE, pp 134–144

Beard C (2018) Diversity and inclusion at mozilla. https://blog.mozilla.org/careers/diversity-and-inclusion-at-mozilla/ . Accessed 2023/04/01

Beneschott B (2014) Is open source open to women? https://www.toptal.com/open-source/is-open-source-open-to-women . Accessed 2023/04/01

Bertagnoli L (2021) How tech can get more women into software engineering. https://builtin.com/software-engineering-perspectives/women-in-engineering . Accessed 2023/04/01

Bird C, Carnahan T, Greiler M (2015) Lessons learned from building and deploying a code review analytics platform. In: 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, IEEE, pp 191–201

Boston University SoPH (2016) Correlation and regression with R. https://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/R/R5_Correlation-Regression/R5_Correlation-Regression4.html . Accessed 29 May 2021

Bosu A, Carver JC (2014) Impact of developer reputation on code review outcomes in oss projects: An empirical investigation. In: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, Association for Computing Machinery, New York, NY, USA, ESEM ’14. https://doi.org/10.1145/2652524.2652544

Bosu A, Sultana KZ (2019) Diversity and inclusion in open source software (oss) projects: Where do we stand? In: Proceedings of the 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

Bosu A, Carver JC, Hafiz M, Hilley P, Janni D (2014) Identifying the characteristics of vulnerable code changes: An empirical study. 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering. China, Hong Kong, pp 257–268

Google Scholar  

Bosu A, Carver JC, Bird C, Orbeck J, Chockley C (2016) Process aspects and social dynamics of contemporary code review: Insights from open source development and industrial practice at microsoft. IEEE Trans Softw Eng 43(1):56–75

Bourke J (2017) Diversity and inclusion: The reality gap. https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2017/diversity-and-inclusion-at-the-workplace.html . Accessed 29 June 2022

Built-in (2021) Diversity + inclusion.what is the meaning of diversity & inclusion? a 2021 workplace guide. https://builtin.com/diversity-inclusion . Accessed 29 May 2021

Burnett M, Stumpf S, Macbeth J, Makri S, Beckwith L, Kwan I, Peters A, Jernigan W (2016) Gendermag: A method for evaluating software’s gender inclusiveness. Interact Comput 28(6):760–787

Burnett M, Counts R, Lawrence R, Hanson H (2017) Gender hcl and microsoft: Highlights from a longitudinal study. In: 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp 139–143. https://doi.org/10.1109/VLHCC.2017.8103461

Calvo D (2020) The (in)visible barriers of free software: Inequalities of online communities in spain. Stud Commun Sci 21. https://doi.org/10.24434/j.scoms.2021.01.011

Canedo E, Bonifacio R, Okimoto M, Serebrenik A, Pinto G, Monteiro E (2020) Work practices and perceptions from women core developers in oss communities. In: Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pp 1–11

Canedo ED, Mendes F, Cerqueira A, Okimoto M, Pinto G, Bonifacio R (2021) Breaking one barrier at a time: How women developers cope in a men-dominated industry. In: Brazilian Symposium on Software Engineering, Association for Computing Machinery, New York, NY, USA, SBES ’21, p 378–387. https://doi.org/10.1145/3474624.3474638

Catolino G, Palomba F, Tamburri D, Serebrenik A, Ferrucci F (2019) Gender diversity and women in software teams: how do they affect community smells? In: Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering, ACM/IEEE, pp 11–20. https://doi.org/10.1109/ICSE-SEIS.2019.00010 . https://2019.icse-conferences.org/home

Ciceri F (2021) Diversity statement. https://www.debian.org/intro/diversity . Accessed 2023/04/01

David PA, Shapiro JS (2008) Community-based production of open-source software: What do we know about the developers who participate? Inf Econ Policy 20(4):364–398. https://doi.org/10.1016/j.infoecopol.2008.10.001 . https://www.sciencedirect.com/science/article/pii/S0167624508000553

Durrleman S, Simon R (1989) Flexible regression models with cubic splines. Stat Med 8(5):551–561

Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensic Secur 9(12):2170–2179

Fan Y, Xia X, Lo D, Li S (2018) Early prediction of merged code changes to prioritize reviewing tasks. Empir Softw Eng 23(6):3346–3393

Forte A, Antin J, Bardzell S, Honeywell L, Riedl J, Stierch S (2012) Some of all human knowledge: Gender and participation in peer production. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work Companion, Association for Computing Machinery, New York, NY, USA, CSCW ’12, p 33–36. https://doi.org/10.1145/2141512.2141530

Foundation (2008) Geek feminisom wiki. http://geekfeminism.wikia.com/wiki/FLOSS#Discussion_of_issues . Accessed 2023/04/01

Frluckaj H, Dabbish L, Widder DG, Qiu HS, Herbsleb J (2022) Gender and participation in open source software development. Proc ACM Hum-Comput Interaction 6(CSCW2):1–31

Fulton LV, Mendez FA, Bastian ND, Musal RM (2012) Confusion between odds and probability, a pandemic? J Stat Educ 20(3)

Ghosh RA (2005) Understanding free software developers: Findings from the FLOSS study. Perspect Free Open Source Softw 28:23–47

Gousios G (2013) The ghtorrent dataset and tool suite. In: Proceedings of the 10th Working Conference on Mining Software Repositories, IEEE Press, Piscataway, NJ, USA, MSR ’13, pp 233–236. http://dl.acm.org/citation.cfm?id=2487085.2487132 . Accessed 2023/04/01

Gousios G, Pinzger M, Deursen Av (2014) An exploratory study of the pull-based software development model. In: Proceedings of the 36th International Conference on Software Engineering, Association for Computing Machinery, New York, NY, USA, ICSE’2014, p 345–355. https://doi.org/10.1145/2568225.2568260

Goyal K, Agarwal K, Kumar R (2017) Face detection and tracking: Using opencv. In: 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), vol 1. IEEE, pp 474–478

Guerrouj L, Baysal O, Lo D, Khomh F (2016) Software analytics: challenges and opportunities. In: Proceedings of the 38th International Conference on Software Engineering Companion, pp 902–903

Harrell F (2015) Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer Series in Statistics, Springer International Publishing. https://books.google.com/books?id=94RgCgAAQBAJ . Accessed 2023/04/01

Harrell F, Lee K, Califf R, Pryor D, Rosati R (1984) Regression modelling strategies for improved prognostic prediction. Stat Med 3(2):143–152. https://doi.org/10.1002/sim.4780030207

Harrell F, Lee K, Matchar D, Reichert T (1985) Harrell jr fe, lee kl, matchar db, reichert taregression models for prognostic prediction: advantages, problems, and suggested solutions. cancer treat rep 69: 1071–1077. Cancer Treat Rep 69:1071–77

Hasan M, Iqbal A, Islam MRU, Rahman AI, Bosu A (2021) Using a balanced scorecard to identify opportunities to improve code review effectiveness: An industrial experience report. Empir Softw Eng 26:1–34

Imtiaz N, Middleton J, Chakraborty J, Robson N, Bai G, Murphy-Hill E (2019). Investigating the effects of gender bias on github. https://doi.org/10.1109/ICSE.2019.00079

Jeong G, Kim S, Zimmermann T, Yi K (2009) Improving code review by predicting reviewers and acceptance of patches. Res Softw Anal Error-Free Comput Center Tech-Memo (ROSAEC MEMO 2009-006) 1:1–18

Jiang Y, Adams B, German DM (2013) Will my patch make it? and how fast? case study on the linux kernel. In: 2013 10th Working Conference on Mining Software Repositories (MSR), IEEE, pp 101–110

Kalliamvakou E, Gousios G, Blincoe K, Singer L, German DM, Damian D (2016) An in-depth study of the promises and perils of mining github. Empir Softw Eng 21(5):2035–2071

Krieger B, Leach J, Nafus D (2006) Gender integrated report of findings. European Union Sixth Framework Programme, Free/Libre/Open Source Software: Policy Support 1(1)

Kononenko O, Baysal O, Guerrouj L, Cao Y (2015) Investigating code review quality: Do people and participation matter? pp 111–120. https://doi.org/10.1109/ICSM.2015.7332457

Laura Sherbin RR (2017) Diversity doesn’t stick without inclusion. https://www.vernamyers.com/2017/02/04/diversity-doesnt-stick-without-inclusion/ . Accessed 2023/04/01

Lee A, Carver JC (2019) Floss participants’ perceptions about gender and inclusiveness: A survey. In: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pp 677–687. https://doi.org/10.1109/ICSE.2019.00077

Lenarduzzi V, Nikkola V, Saarimäki N, Taibi D (2021) Does code quality affect pull request acceptance? an empirical study. J Syst Softw 171:110806

Lin B, Serebrenik A (2016) Recognizing gender of stack overflow users. In: Proceedings of the 13th International Conference on Mining Software Repositories, pp 425–429

Lockwood P (2006) Someone like me can be successful: Do college students need same-gender role models? Psychol Women Q 30(1):36–46

Mansfield ER, Helms BP (1982) Detecting multicollinearity. Am Stat 36(3a):158–160

McIntosh S, Kamei Y, Adams B, Hassan AE (2015) An empirical study of the impact of modern code review practices on software quality. Empir Softw Eng 21. https://doi.org/10.1007/s10664-015-9381-9

Mendez C, Padala HS, Steine-Hanson Z, Hilderbrand C, Horvath A, Hill C, Simpson L, Patil N, Sarma A, Burnett M (2018a) Open source barriers to entry, revisited: A sociotechnical perspective. In: Proceedings of the 40th International Conference on Software Engineering, Association for Computing Machinery, New York, NY, USA, ICSE ’18, p 1004–1015. https://doi.org/10.1145/3180155.3180241

Mendez C, Sarma A, Burnett M (2018b) Gender in open source software: what the tools tell. pp 21–24. https://doi.org/10.1145/3195570.3195572

Menking A, Erickson I, Pratt W (2019) People who can take it: how women wikipedians negotiate and navigate safety. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp 1–14

Mirsaeedi E, Rigby PC (2020) Mitigating turnover with code review recommendation: balancing expertise, workload, and knowledge distribution. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, pp 1183–1195

Nadri R, Rodríguez-Pérez G, Nagappan M (2021) On the relationship between the developer’s perceptible race and ethnicity and the evaluation of contributions in oss. IEEE Trans Softw Eng 48(8):2955–2968

Nafus D (2012) Patches don’t have gender: What is not open in open source software. New Media Soc 14(4):669–683. https://doi.org/10.1177/1461444811422887

Padala SH, Mendez CJ, Dias LF, Steinmacher I, Steine Hanson Z, Hilderbrand C, Horvath A, Hill C, Simpson LD, Burnett M, Gerosa M, Sarma A (2020) How gender-biased tools shape newcomer experiences in oss projects. IEEE Trans Softw Eng 1. https://doi.org/10.1109/TSE.2020.2984173

Parra E, Haiduc S, James R (2016) Making a difference: An overview of humanitarian free open source systems. In: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), pp 731–733

Paul R, Bosu A, Sultana KZ (2019) Expressions of sentiments during code reviews: Male vs. female. pp 26–37. https://doi.org/10.1109/SANER.2019.8667987

Paul R, Turzo AK, Bosu A (2021) Why security defects go unnoticed during code reviews? a case-control study of the chromium os project. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pp 1373–1385

Pourhoseingholi MA, Baghestani AR, Vahedi M (2012) How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench 5(2):79

Prana GAA, Ford D, Rastogi A, Lo D, Purandare R, Nagappan N (2021) Including everyone, everywhere: Understanding opportunities and challenges of geographic gender-inclusion in oss. p 1. https://doi.org/10.1109/TSE.2021.3092813

Qiu Y, Stewart KJ, Bartol KM (2010) Joining and socialization in open source women’s groups: An exploratory study of kde-women. In: Ågerfalk P, Boldyreff C, González-Barahona JM, Madey GR, Noll J (eds) Open Source Software: New Horizons. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 239–251

Chapter   Google Scholar  

Rigby P, German D, Storey MA (2008) Open source software peer review practices. In: 2008 ACM/IEEE 30th International Conference on Software Engineering, pp 541–550. https://doi.org/10.1145/1368088.1368162

Robson N (2018) Diversity and decorum in open source communities. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018, pp 986–987

Santamaría L, Mihaljević H (2018) Comparison and benchmark of name-to-gender inference services. PeerJ Comput Sci 4:e156

Santos A, Vegas S, Oivo M, Juristo N (2019) A procedure and guidelines for analyzing groups of software engineering replications. IEEE Trans Softw Eng PP:1. https://doi.org/10.1109/TSE.2019.2935720

Shull FJ, Carver JC, Vegas S, Juristo N (2008) The role of replications in empirical software engineering. Empir Softw Eng 13(2):211–218

Singh V (2019) Women participation in open source software communities. In: Proceedings of the 13th European Conference on Software Architecture - Volume 2, Association for Computing Machinery, New York, NY, USA, ECSA ’19, pp 94–99. https://doi.org/10.1145/3344948.3344968

Singh V, Brandon W (2019) Open Source Software Community Inclusion Initiatives to Support Women Participation, pp 68–79. https://doi.org/10.1007/978-3-030-20883-7_7

Singh V, Bongiovanni B (2021) Motivated and capable but no space for error women’s experiences of contributing to open source software. In: The international Journal of information, diversity and inclusion, vol 5

Smith TJ, McKenna CM (2013) A comparison of logistic regression pseudo r2 indices. Mult Linear Regression Viewpoints 39(2):17–26

Squire M, Gazda R (2015) Floss as a source for profanity and insults: Collecting the data. In: 2015 48th Hawaii International Conference on System Sciences, IEEE, pp 5290–5298

Sultana S, Bosu A (2021) Are code review processes influenced by the genders of the participants? https://doi.org/10.48550/ARXIV.2108.07774 . https://arxiv.org/abs/2108.07774

Sultana S, Turzo AK, Bosu A (2023) Replication package for Code Reviews in Open Source Projects : How Do Gender Biases Affect Participation and Outcomes? Zeonodo. https://doi.org/10.5281/zenodo.7608539

Tao Y, Han D, Kim S (2014) Writing acceptable patches: An empirical study of open source project patches. In: 2014 IEEE International Conference on Software Maintenance and Evolution, IEEE, pp 271–280

Team FD (2019) Diversity and inclusion in fedora. https://docs.fedoraproject.org/en-US/diversity-inclusion/ . Accessed 2023/04/01

Terrell J, Kofink A, Middleton J, Rainear C, Murphy-Hill E, Parnin C, Stallings J (2017) Gender differences and bias in open source: Pull request acceptance of women versus men. PeerJ Comput Sci 3:e111

Thelwall M, Wilkinson D, Uppal S (2010) Data mining emotion in social network communication: Gender differences in myspace. J Am Soc Inf Sci Technol 61(1):190–199

Thongtanunam P, Tantithamthavorn C, Kula RG, Yoshida N, Iida H, Matsumoto Ki (2015) Who should review my code? a file location-based code-reviewer recommendation approach for modern code review. In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), IEEE, pp 141–150

Thongtanunam P, McIntosh S, Hassan A, Iida H (2016) Review participation in modern code review. Empir Softw Eng 22:768–817

Tourani P, Adams B, Serebrenik A (2017) Code of conduct in open source projects. In: 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp 24–33. https://doi.org/10.1109/SANER.2017.7884606

Vasilescu B, Capiluppi A, Serebrenik A (2014) Gender, representation and online participation : a quantitative study. Interact Comput 26(5):488–511

Vasilescu B, Posnett D, Ray B, van den Brand MG, Serebrenik A, Devanbu P, Filkov V (2015) Gender and tenure diversity in github teams. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp 3789–3798

Veall MR, Zimmermann KF (1994) Evaluating pseudo-r2’s for binary probit models. Qual Quant 28(2):151–164

Vedres B, Vasarhelyi O (2019) Gendered behavior as a disadvantage in open source software development. EPJ Data Sci 8(1):25

Wajcman J (2007) From women and technology to gendered technoscience. Inf Community Soc 10(3):287–298

Wang L, Weinberger K (2020) Reasons for lack of diversity in open source: The case Katie Bouman. Free and Open Technologies

Xia X, Lo D, Wang X, Yang X (2015) Who should review this change?: Putting text and file location analyses together for more accurate recommendations. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, pp 261–270

Yin P, Fan X (2001) Estimating r 2 shrinkage in multiple regression: A comparison of different analytical methods. J Exp Educ 69(2):203–224

Zafar S, Malik MZ, Walia GS (2019) Towards standardizing and improving classification of bug-fix commits. In: 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), IEEE, pp 1–6

Download references

Work conducted by for this research is partially supported by the US National Science Foundation under Grant No. 1850475. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and affiliations.

Department of Computer Science, Wayne State University, Detroit, MI, USA

Sayma Sultana, Asif Kamal Turzo & Amiangshu Bosu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Amiangshu Bosu .

Ethics declarations

Conflicts of interests/competing interests.

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Communicated by: Christoph Treude, Maria Teresa Baldassarre

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Special Issue on Registered Reports .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Sultana, S., Turzo, A.K. & Bosu, A. Code reviews in open source projects : how do gender biases affect participation and outcomes?. Empir Software Eng 28 , 92 (2023). https://doi.org/10.1007/s10664-023-10324-9

Download citation

Accepted : 17 March 2023

Published : 05 June 2023

DOI : https://doi.org/10.1007/s10664-023-10324-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Code review
• Diversity and inclusion
• Pull requests
• Gender bias
• Find a journal
• Publish with us
• Track your research

Using GitHub Codespaces for pull requests

You can use GitHub Codespaces in your web browser, or in Visual Studio Code to create pull requests, review pull requests, and address review comments.

In this article

Using a codespace to work on a pull request gives you all the benefits of GitHub Codespaces. For more information, see " GitHub Codespaces overview ."

About pull requests in GitHub Codespaces

GitHub Codespaces provides you with many of the capabilities you might need to work with pull requests:

• Create a pull request - Using either the Terminal and Git commands or the "Source Control" view, you can create pull requests just as you would on GitHub.com. If the repository uses a pull request template, you'll be able to use this within the "Source Control" view.
• Open a pull request – You can open an existing pull request in a codespace, provided you have codespace access to the branch that is being merged in.
• Review a pull request - Once you have opened a pull request in a codespace, you can use the "GitHub Pull Request" view to add review comments and approve pull requests. You can also use GitHub Codespaces to view review comments .

Opening a pull request in Codespaces

Under your repository name, click Pull requests .

In the list of pull requests, click the pull request you'd like to open in Codespaces.

On the right-hand side of your screen, click Code .

In the Codespaces tab, click .

A codespace is created for the pull request branch and is opened in your default editor for GitHub Codespaces.

Reviewing a pull request in Codespaces

With your default editor set to either Visual Studio Code or Visual Studio Code for Web, open the pull request in a codespace, as described in " Opening a pull request in Codespaces " previously in this article.

In the Activity Bar, click the Git pull request icon to display the "GitHub Pull Request" side bar. This icon is only displayed in the Activity Bar when you open a pull request in a codespace.

If you opened a pull request in a codespace and the pull request icon is not displayed in the Activity Bar, make sure you are signed in to GitHub. Click the GitHub icon in the Activity Bar then click Sign in .

To review the changes that have been made to a specific file, click the file's name in the "GitHub Pull Request" side bar.

This displays a diff view in the editor, with the version of the file from the base branch on the left, and the new version of the file, from the head branch of the pull request, on the right.

To add a review comment, click the + sign next to the line number in the file displayed on the right side of the editor.

Type your review comment and then click Start Review .

Optionally, you can suggest a change that the author of the pull request can click to commit if they agree with your suggestion. To do this, click and hold the + sign next to the first line you want to suggest changing, then drag the + sign to the last line you want to suggest changing. Then click Make a Suggestion in the comment box that's displayed.

The lines you selected are copied into the comment box, where you can edit them to suggest a change. You can add a comment above the line containing ```suggestion to explain your suggested change.

Click Add Comment to add your suggestion to the pull request.

When you are finished adding review comments, you can add a summary comment for your pull request review in the "GitHub Pull Request" side bar. You can then click Comment and Submit , or click the dropdown arrow and select Approve and Submit or Request Changes and Submit .

For more information on reviewing a pull request, see " Reviewing proposed changes in a pull request ."

View comments from a review in Codespaces

Once you have received feedback on a pull request, you can open it in a codespace in your web browser, or in VS Code, to see the review comments . From there you can respond to comments, add reactions, or dismiss the review.

User ratings

Rate this plugin, other plugins from this author, plugins directory » code review.

Code Review is a plugin which lets you annotate source code within the repository browser.

Installation notes

• Copy the plugin into the plugins directory
• Migrate plugin: rake redmine:plugins:migrate RAILS_ENV=production
• Restart Redmine
• Enable the module on the project setting page.
• Goto Code Review setting tab and select the tracker for code reviews.

1.1.1 (2023-10-01)

Compatible with Redmine 5.1.x, 5.0.x.

Download ¶

https://github.com/haru/redmine_code_review/releases/tag/1.1.1

Fixes ¶

• fix sort buttons broken in project settings.
• fix no pencil icons displays with Redmine 5.x.

1.1.0 (2022-03-29)

Compatible with Redmine 5.0.x, 4.2.x, 4.1.x, 4.0.x.

https://github.com/haru/redmine_code_review/releases/tag/1.1.0

Changes ¶

• Compatible with Redmine 5.0
• Add missing "Priority" field on issue creation form
• Fix broken review link in issue window

1.0.0 (2018-12-31)

Compatible with Redmine 4.2.x, 4.1.x, 4.0.x.

https://github.com/haru/redmine_code_review/releases/tag/1.0.0

• Compatible with Redmine 4.0.0

0.9.0 (2017-08-08)

Compatible with Redmine 3.4.x.

https://github.com/haru/redmine_code_review/releases/tag/0.9.0

fixed defects

• Add code review link does not appear in repository browser.
• Delete confirmation dialog does not appear when deleting code review.

0.8.0 (2017-04-17)

Compatible with Redmine 3.3.x, 3.2.x, 3.1.x.

https://bitbucket.org/haru_iida/redmine_code_review/downloads/

• Compatible with Redmine 3.3.x

0.7.0 (2015-03-09)

Compatible with Redmine 3.0.x.

https://bitbucket.org/haru_iida/redmine_code_review/downloads

• Compatible with Redmine 3.0

0.6.5 (2015-01-25)

Compatible with Redmine 2.6.x, 2.5.x, 2.4.x, 2.3.x, 2.2.x, 2.1.x.

Fixed bug ¶

• Pencil icon review replicates in different projects with same filename and commit number

0.6.4 (2014-12-27)

fixed some bugs.

0.6.3 (2013-09-29)

• Fixed problem: parent suggestion does not work in a new review dialog.
• Now $REV and $COMMENTS are enabled in description of auto assignment issue.

0.6.2 (2013-04-04)

Compatible with Redmine 2.3.x, 2.2.x, 2.1.x.

• Compatible with Redmine 2.3.x
• Korean translation updated.
• Now you can embed revision and commit log into subject of assignment mail.
• Code review available in "View all differences".

0.6.1 (2012-12-08)

Compatible with Redmine 2.1.x.

This is bug-fix release.

• Two apply buttons appears when creating a new review.
• Javascript error in revisions view.
• other some minor bug fixes.

0.6.0 (2012-10-27)

• Compatible with Redmine 2.1.x
• Bulgarian translation updated.

0.5.0 (2012-05-31)

Compatible with Redmine 2.0.x.

Compatible with Redmine 2.0.x

0.4.8 (2012-05-31)

Compatible with Redmine 1.4.x.

• Allow selecting a tracker when creating new review.
• fix internal error with ruby 1.9
• Chinese translation updated.

0.4.7 (2012-02-04)

Compatible with Redmine 1.3.x, 1.4.x.

• Multiple SCM of Redmine trunk supported.
• Allow selecting an issue status when creating code review issue.

0.4.6 (2012-01-16)

Compatible with Redmine 1.3.x.

• Compatibility with ChiliProject. Contributed by Andreas Schuh.
• Fixed bug: review issues were not displayed in revision view.
• Fixed bug: Review issues were not displayed in revision view.
• "Code review assignment" issue title include some information about the revision.

Note ¶

This version does not support current trunk redmine(multiple scm).

0.4.5 (2011-12-11)

Compatible with Redmine 1.2.x, 1.3.x.

• Compatible with Redmine 1.3.0
• Creating new review failed if changeset has relation with issue of parent project.
• Review assignment button added on a right side of the revision list.
• Faster patch contributed by mallowlabs.

0.4.4 (2011-10-20)

Compatible with Redmine 1.2.x.

• Now you can edit required custom fields when you create a new review.
• Support development mode.
• German translation updated.
• Code review requester would be set to watcher of review issue automatically.
• Now you can create a new review without filling description field.
• Hide category field from review form if there is no valid category.
• Hide version field from review form if project does not have assignable version.
• and some bugs fixed.

0.4.3 (2011-07-08)

https://bitbucket.org/haru_iida/redmine_code_review/downloads/redmine_code_review-0.4.3.zip

• Bulgarian translation added.
• bug: Can't add review if path includes non-ascii character.
• bug: Illegal link appears if path includes single quote.

0.4.2 (2011-06-11)

• Can't add code review for SVN branches.

0.4.1 (2011-06-11)

• Compatible with Redmine 0.1.2
• Now you can add parent issue id when you create a new code review.

0.4.0 (2011-02-22)

Compatible with Redmine 1.0.x, 1.1.x.

Chandes ¶

• 500 error displayed with empty git repositories.
• ActionView::TemplateError (undefined method `changes' for nil:NilClass) when viewing file
• Illegal characters appears in the link.
• Error when review tracker was not configured.
• Polish translation was added.
• Swedish translation was added.
• Review subjects are displayed in Code Review tab.

0.3.1 (2010-06-23)

http://r-labs.googlecode.com/files/redmine_code_review-0.3.1.zip

  by DB F 8 months ago

  by neo lip almost 4 years ago

  by Ming Li over 5 years ago

Nice plugin, working with 3.4.5. 2018-09-21 update: I found some issue in chrome, and fix it by changing the "\assets\javascripts\code_review.js"

  by Alex Ky about 6 years ago

It creates a lot of redundant Issues instead of just updating the one where the review was requested.

  by Alex Belugin over 6 years ago

  by hai qiu almost 7 years ago

  by asiainfotest asiainfotest about 7 years ago

  by Gergely Szabo over 7 years ago

Vanilla Redmine on Debian 8, not the official deb package: plugin works perfectly.

  by Szabó Gergely over 7 years ago

Debian packages 2.5.2 as 3.0. Blimey! Now the plugin was installed successfully. But once I enable it for a project, it breaks its Setting page forever, can't disable it any more.

  by Francesco V almost 8 years ago

Nice plugin but miss Markdown support (Repository/View file/Add review)

  by Igor A over 8 years ago

Great plugin!

  by Thierry Brochu almost 9 years ago

  by Nicolas Rodriguez about 9 years ago

  by Changsheng Gu over 9 years ago

  by Roy T.Burns over 9 years ago

  by Yukio KANEDA over 9 years ago

  by TridenT Job almost 10 years ago

Easy to use, simple to install.

  by Mario Werner about 10 years ago

Great plugin! A real must have for teams.

  by Matthias Bendewald about 10 years ago

This is just working very good! Thank you for the brilliant plugin

  by Dipan Mehta about 10 years ago

Brilliant work. Most essential plugin.

Please, sign in or register in order to rate this plugin.

Unlimited Access

Exam az-400 topic 5 question 14 discussion.

Your company uses GitHub for source control. The company has a team that performs code reviews. You need to automate the assignment of the code reviews. The solution must meet the following requirements: ✑ Prioritize the assignment of code reviews to team members who have the fewest outstanding assignments. ✑ Ensure that each team member performs an equal number of code reviews in any 30-day period. ✑ Prevent the assignment of code reviews to the team leader. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A. Clear Never assign certain team members.
• B. Select If assigning team members, don't notify the entire team.
• C. Select Never assign certain team members.
• D. Set Routing algorithm to Round robin.
• E. Set Routing algorithm to Load balance.

az_architect

Zuzu_toggler, get it certification.

Unlock free, top-quality video courses on ExamTopics with a simple registration. Elevate your learning journey with our expertly curated content. Register now to access a diverse range of educational resources designed for your success. Start learning today with ExamTopics!

Log in to ExamTopics

Report comment.