mlsanityData to 14 Oct 2026

A parallel corpus of Python functions and documentation strings for automated code documentation and code generation

arXiv 1707.02275 ↗First posted 7 Jul 2017cs.CL cs.AIcited by 57

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Abstract (arXiv)

Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest. Progress in these areas has been limited by the low availability of parallel corpora of code and natural language descriptions, which tend to be small and constrained to specific domains. In this work we introduce a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ("docstrings") generated by scraping open source repositories on GitHub. We describe baseline results for the code documentation and code generation tasks obtained by neural machine translation. We also experiment with data augmentation techniques to further increase the amount of training data. We release our datasets and processing scripts in order to stimulate research in these areas.

Key facts. Cited by 57 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jul 2017.

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Citing papers per year: 2020: 1; 2021: 14; 2022: 16; 2023: 10; 2024: 11; 2025: 4; 2026: 1.

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  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 2,004
  3. 2024CRUXEval: A Benchmark for Code Reasoning, Understanding and Executioncited by 300
  4. 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 189
  5. 2022Natural Language to Code Translation with Executioncited by 124
  6. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  7. 2021Leveraging Automated Unit Tests for Unsupervised Code Translationcited by 82
  8. 2021CoSQA: 20,000+ Web Queries for Code Search and Question Answeringcited by 75
  9. 2022Out of the BLEU: how should we assess quality of the Code Generation models?cited by 65
  10. 2023xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrievalcited by 47
  11. 2024HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generationcited by 39
  12. 2022Fault-Aware Neural Code Rankerscited by 33
38 more
  1. 2021Code Structure Guided Transformer for Source Code Summarizationcited by 30
  2. 2021CodeQA: A Question Answering Dataset for Source Code Comprehensioncited by 30
  3. 2022On the Importance of Building High-quality Training Datasets for Neural Code Searchcited by 30
  4. 2023The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Pythoncited by 29
  5. 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 24
  6. 2024RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translationcited by 23
  7. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  8. 2022Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Codecited by 21
  9. 2023The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generationcited by 20
  10. 2024Analyzing the Performance of Large Language Models on Code Summarizationcited by 19
  11. 2022An Extractive-and-Abstractive Framework for Source Code Summarizationcited by 18
  12. 2026In Line with Context: Repository-Level Code Generation via Context Inliningcited by 18
  13. 2022Python Code Generation by Asking Clarification Questionscited by 17
  14. 2022Source Code Summarization with Structural Relative Position Guided Transformercited by 14
  15. 2022M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarizationcited by 14
  16. 2022Aligning Offline Metrics and Human Judgments of Value for Code Generation Modelscited by 14
  17. 2022Code Recommendation for Open Source Software Developerscited by 13
  18. 2024Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approachcited by 10
  19. 2023Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Modelscited by 8
  20. 2024ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarizationcited by 8
  21. 2024Leveraging Large Language Models in Code Question Answering: Baselines and Issuescited by 6
  22. 2021IOT: Instance-wise Layer Reordering for Transformer Structurescited by 5
  23. 2021Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approachcited by 5
  24. 2021Natural Language-Guided Programmingcited by 4
  25. 2022CodeExp: Explanatory Code Document Generationcited by 4
  26. 2024Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generationcited by 4
  27. 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Qualitycited by 4
  28. 2021ComSum: Commit Messages Summarization and Meaning Preservationcited by 3
  29. 2021GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarizationcited by 3
  30. 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3
  31. 2022Automatic Comment Generation via Multi-Pass Deliberationcited by 3
  32. 2023Neural Machine Translation for Code Generationcited by 3
  33. 2024An evaluation of LLM code generation capabilities through graded exercisescited by 3
  34. 2021End to End Software Engineering Researchcited by 2
  35. 2022GAP-Gen: Guided Automatic Python Code Generationcited by 2
  36. 2022JEMMA: An Extensible Java Dataset for ML4Code Applicationscited by 2
  37. 2021Graph Conditioned Sparse-Attention for Improved Source Code Understandingcited by 1
  38. 2025Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Modelscited by 1

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