mlsanityData to 14 Oct 2026

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

arXiv 2105.12655 ↗First posted 25 May 2021cs.SE cs.AIcited by 200

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Abstract (arXiv)

Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applications ages, it is now more critical than ever to increase software development productivity and modernize legacy applications. Advances in deep learning and machine learning algorithms have enabled numerous breakthroughs, motivating researchers to leverage AI techniques to improve software development efficiency. Thus, the fast-emerging research area of AI for Code has garnered new interest and gathered momentum. In this paper, we present a large-scale dataset CodeNet, consisting of over 14 million code samples and about 500 million lines of code in 55 different programming languages, which is aimed at teaching AI to code. In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques. Additionally, CodeNet provides sample input and output test sets for 98.5% of the code samples, which can be used as an oracle for determining code correctness and potentially guide reinforcement learning for code quality improvements. As a usability feature, we provide several pre-processing tools in CodeNet to transform source code into representations that can be readily used as inputs into machine learning models. Results of code classification and code similarity experiments using the CodeNet dataset are provided as a reference. We hope that the scale, diversity and rich, high-quality annotations of CodeNet will offer unprecedented research opportunities at the intersection of AI and Software Engineering.

Key facts. Cited by 200 papers in the map (data through 14 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 25 May 2021.

Builds on

28 of 49 references.

References · 28

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16 more
  1. 2017Unsupervised Machine Translation Using Monolingual Corpora Onlycited by 273
  2. 2019Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networkscited by 272
  3. 2020Unsupervised Translation of Programming Languagescited by 263
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  11. 2021Directed Acyclic Graph Neural Networkscited by 70
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 20; 2023: 39; 2024: 42; 2025: 54; 2026: 40.

200 papers.

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  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  2. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  3. 2023A Comprehensive Overview of Large Language Modelscited by 648
  4. 2022The Stack: 3 TB of permissively licensed source codecited by 359
  5. 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 238
  6. 2023CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completioncited by 183
  7. 2024Granite Code Models: A Family of Open Foundation Models for Code Intelligencecited by 117
  8. 2023Learning Performance-Improving Code Editscited by 106
  9. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  10. 2021AVATAR: A Parallel Corpus for Java-Python Program Translationcited by 59
  11. 2023CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translationcited by 57
  12. 2024CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferencescited by 57
38 more
  1. 2025SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversioncited by 53
  2. 2025Evaluating the Diversity and Quality of LLM Generated Contentcited by 51
  3. 2024A Performance Study of LLM-Generated Code on Leetcodecited by 49
  4. 2023xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrievalcited by 47
  5. 2023Code Execution with Pre-trained Language Modelscited by 34
  6. 2024Prompting Techniques for Secure Code Generation: A Systematic Investigationcited by 34
  7. 2024Automated Proof Generation for Rust Code via Self-Evolutioncited by 33
  8. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
  9. 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 29
  10. 2024Meta Reasoning for Large Language Modelscited by 29
  11. 2023Refactoring Programs Using Large Language Models with Few-Shot Examplescited by 28
  12. 2024Search-Based LLMs for Code Optimizationcited by 28
  13. 2024ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?cited by 27
  14. 2024PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedbackcited by 24
  15. 2025EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checkingcited by 24
  16. 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generatorscited by 23
  17. 2024CodeEditorBench: Evaluating Code Editing Capability of Large Language Modelscited by 23
  18. 2024RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translationcited by 23
  19. 2024FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Provingcited by 22
  20. 2024Rectifier: Code Translation with Corrector via LLMscited by 20
  21. 2025Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agentscited by 19
  22. 2025Unified Software Engineering Agent as AI Software Engineercited by 19
  23. 2022ContraCLM: Contrastive Learning For Causal Language Modelcited by 18
  24. 2025Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluationcited by 18
  25. 2022FixEval: Execution-based Evaluation of Program Fixes for Programming Problemscited by 17
  26. 2023CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Modelscited by 16
  27. 2023Exploring the Robustness of Large Language Models for Solving Programming Problemscited by 16
  28. 2023TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphscited by 16
  29. 2023Supersonic: Learning to Generate Source Code Optimizations in C/C++cited by 16
  30. 2024InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translationcited by 16
  31. 2025A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic Systemcited by 15
  32. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
  33. 2025CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoningcited by 13
  34. 2024LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Surveycited by 12
  35. 2024ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Searchcited by 12
  36. 2022Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptionscited by 11
  37. 2023RunBugRun -- An Executable Dataset for Automated Program Repaircited by 11
  38. 2023Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++cited by 11

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