ieee software engineering research papers pdf

• © 2022

Software Engineering Research, Management and Applications

• Roger Lee 0

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• Presents recent research in Software Engineering, Management, and Applications
• Is edited outcome of the 20th IEEE/ACIS SERA 2021 conference held May 25-27, 2022, Las Vegas, USA
• Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 1053)

Conference series link(s): SERA: International Conference on Software Engineering Research and Applications

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Conference proceedings info: SERA 2022.

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Table of contents (12 chapters)

Front matter, examining the factors that influence customers’ intention to use smartwatches in malaysia using utaut2 model.

• Norazryana Mat Dawi, Ha Jin Hwang, Ahmad Jusoh, Haeng Kon Kim

Generating Adversarial Robust Defensive CAPTCHA (GARD-CAPTCHA) in Convolutional Neural Networks

• Pu Tian, Weixian Liao, Turhan Kimbrough, Erik Blasch, Wei Yu

A Deep Learning Approach for Lantana Camara Weed Detection and Localization in the Natural Environment

• Wie Kiang Hi, Santoso Wibowo

Modeling Concretizations in Software Design

• Alexey Tazin, Shan Lu, Yanji Chen, Mieczyslaw M. Kokar, Jeff Smith

A Practical Style Guide and Templates Repository for Writing Effective Use Cases

• Bingyang Wei, Lin Deng, Yi Wang

Label Correction of Sound Data with Label Noise Using Self Organizing Map

• Pildong Hwang, Yanggon Kim

Evaluation Method of Enterprise Cybersecurity

• Meng Zhang, Yue Zhou, Che Li, Shuang Li, Jianhua Wu, Chao Yan

A Multi-model Multi-task Learning System for Hurricane Genesis Prediction

• Martin Pineda, Qianlong Wang, Weixian Liao, Michael McGuire, Wei Yu

Development of Autonomous Driving Adaptive Simulation System Using Deep Learning Process Model

• Symphorien Karl Yoki Donzia, Haeng-Kon Kim

An OCL Implementation for Model-Driven Engineering of C++

• R. Maschotta, N. Silatsa, T. Jungebloud, M. Hammer, A. Zimmermann

Improving Students’ Readiness Toward the Labor Market Through Customized Learning

• Majed Almotairi, Hamdan Ziyad Alabsi, Yahya Alqahtani, Mohammed Abdulkareem Alyami, Majed M. Aljazaeri, Yeong-Tae Song

Assessing Software Fault Risk with Machine Learning

• Naveen Ashish, Greg Barish, Steven Minton

Back Matter

This edited book presents scientific results of the 20th IEEE/ACIS International Conference on Software Engineering Research, Management, and Applications (SERA2022) held on May 25, 2022, in Las Vegas, USA. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

The conference organizers selected the best papers from those papers accepted for presentation at the conference.  The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review.From this second round of review, 12 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

• Computational Intelligence
• Software Engineering
• Software Management

Book Title : Software Engineering Research, Management and Applications

Editors : Roger Lee

Series Title : Studies in Computational Intelligence

DOI : https://doi.org/10.1007/978-3-031-09145-2

Publisher : Springer Cham

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Hardcover ISBN : 978-3-031-09144-5 Published: 22 September 2022

Softcover ISBN : 978-3-031-09147-6 Published: 22 September 2023

eBook ISBN : 978-3-031-09145-2 Published: 21 September 2022

Series ISSN : 1860-949X

Series E-ISSN : 1860-9503

Edition Number : 1

Number of Pages : XIII, 204

Number of Illustrations : 20 b/w illustrations, 54 illustrations in colour

Topics : Computational Intelligence , Software Engineering/Programming and Operating Systems , Artificial Intelligence

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IEEE-Comparative Analysis of Software Engineering Models from Traditional to Modern Methodologies.pdf

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— This study deals with a vital and important thing in computer software development. It is concerned with the software management processes that examine the area of software development through the development models, which are known as software development life cycle. It represents the development models namely Waterfall model, Iterative model, V-shaped model, Spiral model, Extreme programming, Iterative and Incremental Method, Rapid prototyping model, The Chaos Model, Adaptive Software Development (ASD), The Agile Software Process (ASP), Crystal, Dynamic System Development Method (DSDM), Feature Driven Development (FDD), Rational Unified Process (RUP), SCRUM, Wisdom, The Big Bang Model. These models have advantages and disadvantages as well. Therefore, the main objective of this study is to represent different models of software development and make comparison between them to show the features and defects of each model.

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IEEE Paper Format | Template & Guidelines

Published on August 24, 2022 by Jack Caulfield . Revised on April 6, 2023.

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Identifying Non-Technical Skill Gaps in Software Engineering Education: What Experts Expect But Students Don’t Learn

As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of software engineering: devoting oneself to continuous learning , being creative by approaching a problem from different angles , and thinking in a solution-oriented way by favoring outcome over ego . Eight follow-up interviews were conducted to further explore how the industry perceives these skill gaps, yielding 26 sub-themes grouped into six bigger themes: stimulating continuous learning , stimulating creativity , creative techniques , addressing the gap in education , skill requirements in industry , and the industry selection process . With this work, we hope to inspire educators to give the necessary attention to the uncovered skills, further mitigating the gap between the industry and the academic world.

Opportunities and Challenges in Code Search Tools

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

Towards an Anatomy of Software Craftsmanship

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.

On the Reproducibility and Replicability of Deep Learning in Software Engineering

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility —whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability —whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks. Method: In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study. Results: Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. Conclusion: It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.

Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.

Software—A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]

Improving bioinformatics software quality through incorporation of software engineering practices

Background Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software. Methodology A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software. Results The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers. Conclusions While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Inter-team communication in large-scale co-located software engineering: a case study

AbstractLarge-scale software engineering is a collaborative effort where teams need to communicate to develop software products. Managers face the challenge of how to organise work to facilitate necessary communication between teams and individuals. This includes a range of decisions from distributing work over teams located in multiple buildings and sites, through work processes and tools for coordinating work, to softer issues including ensuring well-functioning teams. In this case study, we focus on inter-team communication by considering geographical, cognitive and psychological distances between teams, and factors and strategies that can affect this communication. Data was collected for ten test teams within a large development organisation, in two main phases: (1) measuring cognitive and psychological distance between teams using interactive posters, and (2) five focus group sessions where the obtained distance measurements were discussed. We present ten factors and five strategies, and how these relate to inter-team communication. We see three types of arenas that facilitate inter-team communication, namely physical, virtual and organisational arenas. Our findings can support managers in assessing and improving communication within large development organisations. In addition, the findings can provide insights into factors that may explain the challenges of scaling development organisations, in particular agile organisations that place a large emphasis on direct communication over written documentation.

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Study examined AI and SE transdisciplinarity to find ways of aligning them to enable development of AI-SE transdisciplinary theory. Literature review and analysis method was used. The findings are AI and SE transdisciplinarity is tacit with islands within and between them that can be linked to accelerate their transdisciplinary orientation by codification, internally developing and externally borrowing and adapting transdisciplinary theories. Lack of theory has been identified as the major barrier toward towards maturing the two disciplines as engineering disciplines. Creating AI and SE transdisciplinary theory would contribute to maturing AI and SE engineering disciplines.  Implications of study are transdisciplinary theory can support mode 2 and 3 AI and SE innovations; provide an alternative for maturing two disciplines as engineering disciplines. Study’s originality it’s first in SE, AI or their intersections.

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Title: autodev: automated ai-driven development.

Abstract: The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot. However, existing solutions are not leveraging all the potential capabilities available in an IDE such as building, testing, executing code, git operations, etc. Therefore, they are constrained by their limited capabilities, primarily focusing on suggesting code snippets and file manipulation within a chat-based interface. To fill this gap, we present AutoDev, a fully automated AI-driven software development framework, designed for autonomous planning and execution of intricate software engineering tasks. AutoDev enables users to define complex software engineering objectives, which are assigned to AutoDev's autonomous AI Agents to achieve. These AI agents can perform diverse operations on a codebase, including file editing, retrieval, build processes, execution, testing, and git operations. They also have access to files, compiler output, build and testing logs, static analysis tools, and more. This enables the AI Agents to execute tasks in a fully automated manner with a comprehensive understanding of the contextual information required. Furthermore, AutoDev establishes a secure development environment by confining all operations within Docker containers. This framework incorporates guardrails to ensure user privacy and file security, allowing users to define specific permitted or restricted commands and operations within AutoDev. In our evaluation, we tested AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and 87.8% of Pass@1 for code generation and test generation respectively, demonstrating its effectiveness in automating software engineering tasks while maintaining a secure and user-controlled development environment.

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Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., the story behind pixar’s renderman cgi software, first used in toy story , the tools revolutionized animation.

Bill Reeves, an animator at Pixar, working on Toy Story using the company’s RenderMan software.

Watching movies and TV series that use digital visual effects to create fantastical worlds lets people escape reality for a few hours. Thanks to advancements in computer-generated technology used to produce films and shows, those worlds are highly realistic. In many cases, it can be difficult to tell what’s real and what isn’t.

The groundbreaking tools that make it easier for computers to produce realistic images, introduced as RenderMan by Pixar in 1988, came after years of development by computer scientists Robert L. Cook , Loren Carpenter , Tom Porter , and Patrick M. Hanrahan . RenderMan, a project launched by computer graphics pioneer Edwin Catmull , is behind much of today’s computer-generated imagery and animation, including in the recent fan favorites Avatar: The Way of Water , The Mandalorian , and Nimona .

The technology was honored with an IEEE Milestone in December during a ceremony held at Pixar’s Emeryville, Calif., headquarters. The ceremony is available to watch on demand .

“I feel deeply honored that IEEE recognizes this achievement with a Milestone award,” Catmull, a Pixar founder, said at the ceremony. “Everyone’s dedication and hard work [while developing the technology] brought us to this moment.”

Cook, Carpenter, and Porter collaborated as part of Lucasfilm ’s computer graphics research group, an entity that later became Pixar. Hanrahan joined Pixar after its launch. The development of the software that would eventually become RenderMan started long before.

From Utah and NYIT to Lucasfilm

As a doctoral student studying computer science at the University of Utah , in Salt Lake City, Catmull developed the principle of texture mapping, a method for adding complexity to a computer-generated surface. It later was incorporated into RenderMan.

After graduation, Catmull joined the New York Institute of Technology on Long Island as director of its recently launched computer graphics research program. NYIT’s founder, Alexander Schure , started the program with the goal of using computers to create animated movies. Malcolm Blanchard , Alvy Ray Smith , and David DiFrancesco soon joined the lab.

While at the University of Utah, Blanchard designed and built hardware that clipped 3D shapes to only what was potentially visible. Before joining NYIT, Smith, an IEEE life member, helped develop SuperPaint at the Xerox Palo Alto Research Center in California. It was one of the first computer raster graphics editor programs. DiFrancesco, an artist and scientist, worked with Smith on the project.

“I feel deeply honored that IEEE recognizes this achievement with a Milestone award.” —Edwin Catmull

During the next five years the team created so many pioneering rendering technologies that when Catmull tried to list all its achievements years later, he opted to stop at four pages, according to an article on the history of Pixar .

The team’s technologies include Tween, software that enables a computer to automatically generate the intermediate frames between key frames of action; and the Alpha Channel , which combines separate elements into one image.

“We didn’t keep [our work] secret,” Catmull said in an interview with The Institute . The team created a 22-minute short using its technology. It soon caught the attention of Hollywood producer George Lucas , founder of Lucasfilm and originator of the Star Wars and Indiana Jones franchises.

Lucas, aiming to digitize the film production process, recruited Catmull in 1979 to head the company’s newly created computer division. He tasked the group with developing a digital, nonlinear film editing system, a digital sound editing system, and a laser film printer.

During the next year, Catmull brought Smith, DiFrancesco, and Blanchard to join him.

But Smith, who became director of the division’s graphics group, soon realized that Lucas didn’t fully understand what computer graphics could do for the film industry, he told The Institute in 2018. To show Lucas what was possible, the team decided to develop a rendering program that could create complex, photorealistic imagery virtually indistinguishable from filmed live action images.

They started a rendering team to develop the program. In 1981 Carpenter and Cook came on board. Carpenter had been working in the computer-aided design group at Boeing , in Seattle, where he developed procedural modeling. The algorithm creates 3D models and textures from sets of rules.

Cook, then a recent graduate of Cornell , had published a paper on why nearly every computer-generated object had a plasticlike appearance. His article found its way to Catmull and Smith, who were impressed with his research and offered Cook a job as a software engineer.

And then the project that was to become RenderMan was born.

From REYES to RenderMan

The program was not always known as RenderMan. It originally was named REYES (render everything you ever saw).

Carpenter and Cook wanted REYES to create scenery that mimicked real life, add motion blur, and eliminate “jaggies,” the saw-toothed curved or diagonal lines displayed on low-resolution monitors.

No desktop computer at the time was fast enough to process the algorithms being developed. Carpenter and Cook realized they eventually would have to build their own computer. But they first would have to overcome two obstacles, Cook explained at the Milestone ceremony.

“Computers like having a single type of object, but scenes require many different types of objects,” he said. Also, he added, computers “like operating on groups of objects together, but rendering has two different natural groupings that conflict.”

Those are shading (which you do on every point on the same surface) and hiding (the things you do at every individual pixel).

Carpenter created the REYES algorithm to resolve the two issues. He defined a standard object and called it a micropolygon , a tiny, single-color quadrilateral less than half a pixel wide. He figured about 80 million micropolygons were needed per the 1,000 polygons that typically made up an object. Then he split the rendering into two steps: one to calculate the colors of the micropolygons and the other to use them to determine the pixel colors.

To eliminate jaggies , Cook devised the so-called Monte Carlo technique . It randomly picks points —which eliminates jaggies and the interference of light. Noise patterns appeared, however.

“But there is another way you can pick points,” Cook explains. “You pick a point randomly, and the next point randomly, but you throw the next one out if it is too close to the first point.”

Problems RenderMan Had to Solve

Computer scientists Robert L. Cook and Loren Carpenter took on the initial challenge of developing the computer graphics software that became Renderman. They created this list of tasks for the tool:

• Create complex scenes, with several different computer-generated objects in a scene to mimic real life.
• Give texture to wooden, metallic, or liquid objects. (At the time, computer-generated objects had a plasticlike appearance.)
• Eliminate jaggies, the saw-toothed appearance of curved or diagonal lines when viewed on a low-resolution monitor. They appear at an object’s edges, mirror reflection, and the bending of light rays when they pass through transparent substances such as glass or water.
• Create motion blur or the apparent streaking of moving objects caused by rapid movement.
• Simulate the depth-of-field effect, in which objects within some range of distances in a scene appear in focus, and objects nearer or farther than this range appear out of focus.
• Generate shadows only in the direction of the light source.
• Create reflections and refractions on shiny surfaces.
• Control how deep light penetrates the surface of an object.

That is known as a Poisson disk distribution, modeled on the distribution of cells in the human retina—which also have a seemingly random pattern but with a consistent minimum spacing.

The Monte Carlo change eliminated the annoying visual effects. The distribution also simplified several other tasks handled by the rendering software, including the creation of motion blur and a simulated depth-of-field effect.

“Creating motion blur was probably the single hardest problem we had,” Catmull told IEEE Spectrum in a 2001 interview . Porter, who was part of the graphics group, came up with a way to use the random sampling notion to solve motion blur. In addition to determining what colors appear at each point, the computer considers how that changes over time. Using point sampling, Cook explained, the group found a simple solution: “To every one of your random samples, you assign a random time between when a traditional camera’s shutter would open and when that shutter would close.” Averaging the times creates a blurred image.

The same process worked to simulate the depth-of-field effect: an image in which some areas are in focus and some are not. To create the lack of focus using point sampling, the software assigns a random spot on an imaginary lens to each randomly selected point.

They initially tried the software on a VAX 11/780 computer, running at 1 million instructions per second. But it was too slow, so the team developed the Pixar image computer . It executed instructions at a rate of 40 MIPS, making it 200 times faster than the VAX 11/780, according to the Rhode Island Computer Museum .

Pixar was funded thanks to Steve Jobs

In 1984 the graphics group invited animator John Lasseter to direct the first short film using REYES. The Adventures of André & Wally B. featured a boy who wakes in a forest after being annoyed by a pesky bumblebee. The movie premiered at the Association for Computing Machinery ’s 1984 Special Interest Group on Computer Graphics and Interactive Techniques conference.

After the premiere, visual effects studio Industrial Light and Magic asked the team to create the first CGI-animated character to be used in a live-action feature-length film. The group developed the stained-glass knight character for the 1985 Young Sherlock Holmes movie.

In 1986 the Lucasfilm graphics group, now with 40 employees, was spun out into a separate company that went on to become Pixar.

“At first it was a hardware company,” Smith says. “We turned a prototype of the Pixar image computer into a product. Then we met with some 35 venture capitalists and 10 companies to see if they would fund Pixar. Finally, Steve Jobs signed on as our majority shareholder.”

REYES contained a custom-built interface that had been written to work with software used by Lucasfilm, so the teams needed to replace it with a more open interface that would work with any program sending it a scene description, according to the Milestone entry on the Engineering and Technology History Wiki .

In 1988, with the help of computer graphics pioneer Hanrahan, Pixar scientists designed a new interface. Hanrahan helped refine shading language concepts as well. Thanks to a conversation that Hanrahan had about futuristic rendering software being so small it could fit inside a pocket, like a Sony   Walkman , REYES became RenderMan. Pixar released the tool in 1988.

Three years later, Pixar and Walt Disney Studios partnered to “make and distribute at least one computer-generated animated movie,” according to Pixar’s website . The first film was Toy Story , released in 1995. The first fully computer-generated animated movie, it became a blockbuster.

RenderMan is still the standard rendering tool used in the film industry. As of 2022, the program had been used in more than 500 movies. The films it helped create have received 15 Academy Awards for Best Animated Feature and 26 for Best Visual Effects.

In 2019 Catmull and Hanrahan received the Turing Award from the Association for Computing Machinery for “fundamental contributions to 3-D computer graphics and the revolutionary impact of these techniques on computer-generated imagery in filmmaking and other applications.”

A plaque recognizing the technology is displayed next to the entrance gates at Pixar’s campus. It reads:

RenderMan software revolutionized photorealistic rendering, significantly advancing the creation of 3D computer animation and visual effects. Starting in 1981, key inventions during its development at Lucasfilm and Pixar included shading languages, stochastic antialiasing, and simulation of motion blur, depth of field, and penumbras. RenderMan’s broad film industry adoption following its 1988 introduction led to numerous Oscars for Best Visual Effects and an Academy Award of Merit for its developers.

Administered by the IEEE History Center and supported by donors , the Milestone program recognizes outstanding technical developments around the world.

The IEEE Oakland–East Bay Section in California sponsored the nomination, which was initiated by IEEE Senior Member Brian Berg , vice chair of the section’s history committee.

“It’s important for this history to be documented,” Smith told The Institute , “and I see that as one of the roles of IEEE: recording the history of its own field.

“Engineers and IEEE members often don’t look at how technological innovations affect the people who were part of the development. I think the [Milestone] dedication ceremonies are special because they help highlight not just the tech but the people surrounding the tech.”

• A Birthday For Pixar’s RenderMan Software ›
• Q&A With Alvy Ray Smith, Cofounder of Pixar ›
• The Real Story of Pixar ›

Joanna Goodrich is the associate editor of The Institute , covering the work and accomplishments of IEEE members and IEEE and technology-related events. She has a master's degree in health communications from Rutgers University, in New Brunswick, N.J.

Thanks for this great story! Working with the team of Ed Catmull, Alvy Ray Smith, Rob Cook, Tom Porter, Loren Carpenter, and Pat Hanrahan on this Milestone was an amazing experience. It originated from a conversation I had with Catmull in 2013 when he became a Fellow of the Computer History Museum, but Ed was too busy making movies to spend time on it. It came together while we worked on the Milestone for the University of Utah, which was dedicated one year ago and as discussed in https://spectrum.ieee.org/history-of-computer-graphics-industry

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