# A Library for Representing Python Programs as Graphs for Machine Learning

arXiv: https://arxiv.org/abs/2208.07461 · HTML: https://mlsanity.com/paper/2208.07461/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 30 of 30)

- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [Code Prediction by Feeding Trees to Transformers](https://mlsanity.com/paper/2003.13848/) (2020)
- [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021)
- [Self-Supervised Bug Detection and Repair](https://mlsanity.com/paper/2105.12787/) (2021)
- [Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks](https://mlsanity.com/paper/2010.12621/) (2020)
- [Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs](https://mlsanity.com/paper/2103.09499/) (2021)
- [Learning to Fix Build Errors with Graph2Diff Neural Networks](https://mlsanity.com/paper/1911.01205/) (2019)
- [ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking](https://mlsanity.com/paper/1912.02972/) (2019)
- [InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees](https://mlsanity.com/paper/2012.07023/) (2020)
- [Learning Semantic Program Embeddings with Graph Interval Neural Network](https://mlsanity.com/paper/2005.09997/) (2020)
- [Fault Localization with Code Coverage Representation Learning](https://mlsanity.com/paper/2103.00270/) (2021)
- [Learning to Represent Programs with Heterogeneous Graphs](https://mlsanity.com/paper/2012.04188/) (2020)
- [HEAT: Hyperedge Attention Networks](https://mlsanity.com/paper/2201.12113/) (2022)
- [Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions](https://mlsanity.com/paper/2203.03771/) (2022)
- [Learning Graph Structure With A Finite-State Automaton Layer](https://mlsanity.com/paper/2007.04929/) (2020)
- [Scalpel: The Python Static Analysis Framework](https://mlsanity.com/paper/2202.11840/) (2022)
- [Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction](https://mlsanity.com/paper/1802.04986/) (2018)
- [Path-Based Function Embedding and its Application to Specification Mining](https://mlsanity.com/paper/1802.07779/) (2018)
- [Modular Tree Network for Source Code Representation Learning](https://mlsanity.com/paper/2104.00196/) (2021)
- [Learning to Extend Program Graphs to Work-in-Progress Code](https://mlsanity.com/paper/2105.14038/) (2021)
- [The Correlation among Software Complexity Metrics with Case Study](https://mlsanity.com/paper/1408.4523/) (2014)
- [Using GGNN to recommend log statement level](https://mlsanity.com/paper/1912.05097/) (2019)

## Built on by (most cited first, 4 of 4)

- [Transformers Meet Directed Graphs](https://mlsanity.com/paper/2302.00049/) (2023)
- [COMEX: A Tool for Generating Customized Source Code Representations](https://mlsanity.com/paper/2307.04693/) (2023)
- [CONCORD: Towards a DSL for Configurable Graph Code Representation](https://mlsanity.com/paper/2401.17967/) (2024)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)

## Abstract (arXiv)

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.