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

7 Jul 2017IJCNLP 2017 11arXiv:1707.02275archive 2025-07-28

Antonio Valerio Miceli Barone, Rico Sennrich

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.

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Avmb/code-docstring-corpus officialmentioned in papermentioned on GitHub report
ICSEG/M2TS mentioned on GitHubpytorch report
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2ran · our draft was wrong
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escape_control_strings EdinburghNLP/code-docstring-corpus/scripts/extract_funcdefs_and_docstrings.py community (archive-listed) ran · our draft was wrong fingerprinted licence not identified · pointer only · 35de1e465eaa1e08 · report
prettify_docstring EdinburghNLP/code-docstring-corpus/scripts/extract_funcdefs_and_docstrings.py community (archive-listed) ran · our draft was wrong fingerprinted licence not identified · pointer only · db41d227f3c64b44 · report
reduce_ident EdinburghNLP/code-docstring-corpus/scripts/extract_funcdefs_and_docstrings.py community (archive-listed) unverified licence not identified · pointer only · 3bb44e9a94712fd7 · report

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Code GenerationData AugmentationMachine TranslationTranslation

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