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arXiv · 2208.07461

A Library for Representing Python Programs as Graphs for Machine Learning

David Bieber·Kensen Shi·Petros Maniatis·Charles Sutton·Vincent Hellendoorn·Daniel Johnson·Daniel Tarlow
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Abstract

Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite ``program graphs'' that combine control-flow, data-flow, syntactic, and lexical information about a program. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.

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David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent Hellendoorn, Daniel Johnson, Daniel Tarlow. 2022-08-15. A Library for Representing Python Programs as Graphs for Machine Learning. https://arxiv.org/abs/2208.07461

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