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Code Generation Using Machine Learning: A Systematic Review

Authors: Enrique Dehaerne, Bappaditya Dey, Sandip Halder, Stefan De Gendt, Wannes Meert

Published in IEEE Xplore 04 August 2022 View in IEEE Xplore

Recently, machine learning (ML) methods have been used to create powerful language models for a broad range of natural language processing tasks. An important subset of this field is that of generating code of programming languages for automatic software development. This review provides a broad and detailed overview of studies for code generation using ML. We selected 37 publications indexed in arXiv and IEEE Xplore databases that train ML models on programming language data to generate code. The three paradigms of code generation we identified in these studies are description-to-code, code-to-description, and code-to-code. The most popular applications that work in these paradigms were found to be code generation from natural language descriptions, documentation generation, and automatic program repair, respectively. The most frequently used ML models in these studies include recurrent neural networks, transformers, and convolutional neural networks. Other neural network architectures, as well as non-neural techniques, were also observed. In this review, we have summarized the applications, models, datasets, results, limitations, and future work of 37 publications. Additionally, we include discussions on topics general to the literature reviewed. This includes comparing different model types, comparing tokenizers, the volume and quality of data used, and methods for evaluating synthesized code. Furthermore, we provide three suggestions for future work for code generation using ML.

View this article on IEEE Xplore

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Special Issue: Advances in Machine Learning and Deep Neural Networks

Volume 109, Issue 5

Guest Editors

Guest Editors: Rama Chellappa, Sergios Theodoridis, and Andre van Schaik

Special Issue Papers

Scanning the issue.

By R. Chellappa, S. Theodoridis, and A. van Schaik

Toward Causal Representation Learning

By B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio

This article reviews fundamental concepts of causal inference and relates them to crucial open problems of machine learning, including transfer learning and generalization, thereby assaying how causality can contribute to modern machine learning research.

Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness

By G. Ortiz-Jiménez, A. Modas, S.-M. Moosavi-Dezfooli, and P. Frossard

This article presents an in-depth review of the field of adversarial robustness in deep learning and provides a self-contained introduction to its main notions.

Graph Neural Networks: Architectures, Stability, and Transferability

By L. Ruiz, F. Gama, and A. Ribeiro

This article deals with graph neural networks (GNNs) that operate on data supported on graphs.

Mathematical Models of Overparameterized Neural Networks

By C. Fang, H. Dong, and T. Zhang

The focus of this article is on theoretical developments concerning the analysis of overparameterized neural networks.

Mad Max: Affine Spline Insights Into Deep Learning

By R. Balestriero and R. G. Baraniuk

In this article, the bridge between deep networks (DNs) and approximation theory via spline functions and operators is rigorously established.

Tropical Geometry and Machine Learning

By P. Maragos, V. Charisopoulos, and E. Theodosis

This article deals with tropical geometry that has recently emerged as a tool in the analysis and extension of several classes of problems in both classical machine learning and deep learning.

A Unifying Review of Deep and Shallow Anomaly Detection

By L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller

This article deals with application of deep learning techniques to anomaly detection. Furthermore, connections between classic “shallow” and novel deep approaches are established, and it is shown how this relation might cross-fertilize or extend both directions.

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

By J. Park, S. Samarakoon, A. Elgabli, J. Kim, M. Bennis, S.-L. Kim, and M. Debbah

The goal of this article is to provide a holistic overview of relevant communication and machine learning (ML) principles, and thereby present communication-efficient and distributed ML frameworks with selected use cases.

A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

By S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert, and R. M. Summers

In this article, the authors highlight both clinical needs and technical challenges in medical imaging and describe how emerging trends in deep learning are addressing these issues.

Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications

By M.-Y. Liu, X. Huang, J. Yu, T.-C. Wang, and A. Mallya

This article provides an overview of generative adversarial networks (GANs) with a special focus on algorithms and applications for visual synthesis.

Tensor Methods in Computer Vision and Deep Learning

By Y. Panagakis, J. Kossaifi, G. G. Chrysos, J. Oldfield, M. A. Nicolaou, A. Anandkumar, and S. Zafeiriou

This article provides an overview of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications.

Computational Media Intelligence: Human-Centered Machine Analysis of Media

By K. Somandepalli, T. Guha, V. R. Martinez, N. Kumar, H. Adam, and S. Narayanan

The topic treated in this article is the application of deep learning algorithms, combined with audio-visual signal processing, to analyze entertainment media such as film/TV.

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

By M. Davies, A. Wild, G. Orchard, Y. Sandamirskaya, G. A. Fonseca Guerra, P. Joshi, P. Plank, and S. R. Risbud

This article provides a survey of results obtained to date with Intel’s Loihi across the major algorithmic domains under study, including deep-learning approaches as well as novel approaches that aim to harness the key features of spike-based neuromorphic hardware more directly.

Brain-Inspired Learning on Neuromorphic Substrates

By F. Zenke and E. O. Neftci

This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates.

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Machine Learning Project Topics With Abstracts and Base Papers 2024

Embark on a journey into the realm of machine learning with our curated list of M.Tech project topics for 2024, complemented by trending IEEE base papers. These projects cover a spectrum of innovative applications and advancements in the field, offering an invaluable resource for M.Tech students seeking to push the boundaries of knowledge and skill. Our comprehensive collection encompasses diverse Machine Learning project topics, each accompanied by a meticulously selected base paper and a concise abstract. From natural language processing and computer vision to reinforcement learning and predictive analytics, these projects reflect the latest trends in the ever-evolving landscape of artificial intelligence. Stay ahead of the curve by exploring projects that align with the current demands and challenges faced by industries worldwide. Whether you are a student, researcher, or industry professional, our compilation serves as a gateway to the forefront of machine learning innovation. The project titles are strategically chosen to include relevant keywords, ensuring alignment with the latest IEEE standards and technological advancements. Dive into the abstracts to gain a quick insight into the scope, methodology, and potential impact of each project.

M.Tech Projects Topics List In Machine Learning

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Machine Learning

• Reports substantive results on a wide range of learning methods applied to various learning problems.
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IEEE Python Projects 2023 2024 Machine Learning Projects, Deep Learning Projects, Artificial Intelligence Titles, Data Science Project Ideas for Final Year 2023 2024

Python Projects 2023 Machine Learning Projects, Artificial Intelligence, Deep Learning Projects title, Data Science project ideas for Final Year IEEE Projects 2023 – 2024 Ieee machine learning Projects Ieee machine learning Projects 

IEEE python projects machine learning 2021 2023 Final Year  Python projects 2023 2023 IEEE machine learning Projects 

Python Projects source code 2023 2023

Ieee papers on python machine learning final year projects ieee machine learning projects for final year with source code in python ieee projects for cse 2023 2024, what is machine learning.

Basically, An ML model is defined as a computer-intensive mechanism and applies resampling and iterative methodologies for classification approaches. E specially  ML approaches are considered with optimal subset selection and eliminate the issues of classical classifiers like over-fitting as well as distributional demands of parameters. F ollowing ML technologies that have emerged in computer science with logic and basic mathematics, statistics, as ML approaches do not estimate the group, features rather it is initialized with an arbitrary group separator and tunes frequently till satisfying the classification groups. S ignificantly ML examines the tuning variables and individual ML functions that became unstable, B alanced against which makes a suitable process. F urthermore, As the non-statistical nature is embedded, overall these approaches can apply the data in various formats like nominal data that generates maximum classification accuracies.

Project on Machine Learning 

In this Section S pecifically, we have discussed about some of the projects on Machine Learning with current trends.

Predicting Poverty Level from Satellite Imagery

Determining the poverty levels of various regions throughout the world is still crucial in identifying interventions for poverty reduction initiatives and directing resources fairly. G enerally , reliable data on global economic livelihoods is hard to come by, especially for areas in the developing world, hampering efforts to both deploy services and monitor/evaluate progress. H owever, t he current challenge in this domain is that agencies across the world that predict income levels take a huge amount of time to do the same.

I nstead of a Manual Human Taking process, This project proposes to use satellite images to detect economic activity and, as a result, estimate poverty in a location. lastly, A   Recurrent neural network   is trained to learn various developmental parameters like   rooftop type, source of lighting, proximity to water sources, Agriculture Areas, Road Structure, and Industrial Areas . L ikewise, it will estimate the poverty level of each Area using Satellite Images. particularly it will cost you less amount as well comparatively less time as well.

Phishing Website Monitoring Using ML

In this project, we propose a feature-free method for detecting phishing websites correspondingly based on a similarity measure that computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. A dditionally, It also removes any dependence on a specific set of website features. A ltogether t his method examines the HTML of webpages and computes their similarity with known phishing websites during testing phases , in order to classify them.

F urthermore, We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing web pages. I dentically We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. L astly, On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. P articularly Our approach uses prototypes, eliminating the need to retain long-term data in the future, and simultaneously is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.

Food Recognition and Calorie Measurement using Machine Learning

Presently This is one of the projects particularly to discuss the relationship between nutritional ingredients identification in food and inspecting Calories through Machine Learning models to perform the data analysis, S ignificantly   the experiments on real-life datasets show that our method improves the performance with efficient accuracy . S pecifically , Our system will recommend food for some Different Age groups. S ubsequently, Our work is able to identify the Nutrition that we may get affected by lacking certain nutritional ingredients in our body and recommends the food that can benefit the rehabilitation of those Age Groups. T hereafter To achieve high accuracy and low time complexity, the proposed system was implemented using CNN Machine Learning models. L astly, The model, when trained convolutionally, generates the natural image samples which give a better broad statistical structure of the natural images as compared with comparatively existing parametric generative methods.

Train Delay Prediction using Machine Learning

Eventually Delay prediction is a process of estimating delay probability based on formerly known data at a given checkpoint and is typically measured via arrival (departure) delay. Furthermore the key to making delay predictions based on actual operational data involves establishing the relationship between train delays and various characteristics of a railway system. Henceforth this provides a basis for the operator’s scheduling decision Train delay is a significant problem that negatively impacts the railway industry and costs billions of dollars each year. Simultaneously In this project we have used Train delay dataset from IRTC to predict Train delays. Specifically  We have used Faster RCNN algorithm to predict flight departure delay and thereupon our model can identify which features were more important when predicting Train delays.

Blood Grouping Detection Using Image Processing

Basically, there is worldwide demand for an affordable Blood Group measurement solution, although this is a particularly urgent need in developing countries. Altogether Image Processing, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Comparatively This Project proposes a noninvasive Blood Group measurement process.  Also, it compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, Image Processing algorithms, and Detection models to calculate Blood Groups. Especially This analysis was then used to recommend realistic approaches to build an Image Processing-based point-of-care tool for Blood Group measurement in a non-invasive manner. Certainly, This project proposes approaches for blood Group measurement with the aim of recommending data collection techniques, signal extraction processes, feature calculation processes, and Image Processing algorithms for developing a noninvasive Blood Group estimation using an Image.

Parkinson’s Disease Detection Using Spiral Images

Correspondingly Parkinson’s Disease (PD) is a progressive neurodegenerative disorder emphatically, which is characterized by  Various symptoms. F urthermore, the progressive neurodegenerative disorder affects the nervous system in the elderly, which is characterized by motor symptoms such as tremors, rigidity, slowness of movement, and problems with gait. Obviously, In this work, an attempt has been made to classify the spiral images of healthy control and Parkinson’s disease subjects using deep-learning neural networks. P articularly The Vision-based Convolutional Neural Network architecture is used to refine the diagnosis of neurodegenerative disorder disease.

Nevertheless, This project proposes a Vision-Based novel deep learning architecture for neuro-generative disorder screening. obviously, this project, analysis of Spiral images for discrimination of healthy control and NDD (Neurodegenerative disorder) subjects is attempted using the CNN model. The proposed FAST-RCNN exploits Feature Extraction to tackle multi-view data from the Spiral Image data. significantly training, the proposed model employs a data enhancement technology called SCI-KIT’s Image Data Generator API on multi-view data.

Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning

Since the coronavirus has shown up, the inaccessibility of legitimate clinical resources is at its peak, previously like the shortage of specialists and healthcare workers, secondly lack of proper equipment and medicines, etc. Specifically, The entire medical fraternity is in distress, which results in numerous individuals’ demise. unlike Due to unavailability, individuals started taking medication independently without appropriate consultation, making their health condition worse than usual. Identically As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation.

Meanwhile, This project intends to present a drug recommender system that can drastically reduce specialists’ heap. Regardless In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. specifically, The predicted sentiments were evaluated by precision, recall, F1 score, accuracy, and AUC score. whereas The results show that classifier DNN using TF-IDF vectorization outperforms all other models with 98% accuracy.

Stress Detection In IT Professionals by Image Processing and Machine Learning

Significantly The main motive of our project is to detect stress in IT professionals using vivid Machine learning and Image processing techniques. obviously, Our system is an upgraded version of the old stress detection systems which excluded live detection and personal counseling but this system comprises of live detection and periodic analysis of employees and detects physical as well as mental stress levels in his/her by providing them with proper remedies for managing stress by providing survey form periodically. Moreover, Our system mainly focuses on managing stress and making the working environment healthy and spontaneous for the employees and furthermore it getting the best out of them during working hours.

Henceforth The proposed System Machine Learning algorithms like KNN classifiers are applied to classify stress. Following Image Processing is used at the initial stage for detection, the employee‟s image is given by the browser which serves as input. further, In order to get an enhanced image or to extract some useful information from it image processing is used by converting image into digital form and performing some operations on it. Generally By taking input as an image and output may be image or characteristics associated with that images. The emotion are displayed on the rounder box. The stress level indicated by Angry, Disgusted, Fearful, Sad.

Human Authentication using Gait Recognition (Walking Style)

Evidently, Biometric identification like fingerprints, retina, palm, and voice recognition needs the subject’s permission and physical attention. Correspondingly Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. Basically, The purpose of this Project is to detect humans based on their Waling styles. We first extract the gait features from image sequences using the Feature Module. Features are then trained based on the frequencies of these feature trajectories, from which recognition is performed.

Gait recognition is the process where the features of human motion are automatically obtained/extracted and later these features enable us to authenticate the identity of the person in motion. Besides As with other pattern recognition techniques, the gait recognition technique also involves 2 stages: F irstly Information is derived from human locomotion in the first stage i.e. feature extraction stage. F ormerly In the next stage, i.e. the recognition stage, a standard similarity computation technique (Incremental Component Analysis) is used to obtain results for being a match or a mismatch.

Decentralized Voting system using Blockchain

Certainly, Electronic voting or e-voting has been used in varying forms since the 1970s with fundamental benefits over paper-based systems such as increased efficiency and reduced errors. However, there remain challenges to achieving widespread adoption of such systems, especially with respect to improving their resilience against potential faults. Generally, Blockchain is a disruptive technology of the current era and promises to improve the overall resilience of e-voting systems. Likewise, This project presents an effort to leverage the benefits of blockchain such as cryptographic foundations and transparency to achieve an effective scheme for e-voting.

Moreover, The proposed scheme conforms to the fundamental requirements for e-voting schemes and achieves end-to-end verifiability. The paper presents details of the proposed e-voting scheme along with its implementation using the Multichain platform. Obviously, The project presents an in-depth evaluation of the scheme which successfully demonstrates its effectiveness to achieve particularly an end-to-end verifiable e-voting scheme. Ieee machine learning Projects

Diabetic Retinopathy Detection Using Machine Learning

Diabetic Retinopathy is the most common cause of vision loss among people particularly diabetes and the leading cause of vision impairment and blindness among working-age adults. S econdly, By using a certain algorithm the retinal image from the user is fed into the system. significantly The blood vessels are extracted from the image then it is pre-processed by filtering and segmentation process. Rather It is followed by fractional edge reduction which is used for the feature extraction and by using a Faster retinal convolutional neural network algorithm to automate the diagnosis process. It improves the resultant accuracy and by this classification technique,overall we can achieve high accuracy. Ieee machine learning Projects

AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

Ray Perrault Co-director, AI Index

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