Publication Type

Conference Paper

Version

acceptedVersion

Publication Date

12-2019

Abstract

Program comprehension is a fundamental task in software development and maintenance processes. Software developers often need to understand a large amount of existing code before they can develop new features or fix bugs in existing programs. Being able to process programming language code automatically and provide summaries of code functionality accurately can significantly help developers to reduce time spent in code navigation and understanding, and thus increase productivity. Different from natural language articles, source code in programming languages often follows rigid syntactical structures and there can exist dependencies among code elements that are located far away from each other through complex control flows and data flows. Existing studies on tree-based convolutional neural networks (TBCNN) and gated graph neural networks (GGNN) are not able to capture essential semantic dependencies among code elements accurately. In this paper, we propose novel tree-based capsule networks (TreeCaps) and relevant techniques for processing program code in an automated way that encodes code syntactical structures and captures code dependencies more accurately. Based on evaluation on programs written in different programming languages, we show that our TreeCaps-based approach can outperform other approaches in classifying the functionalities of many programs.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Workshop on Machine Learning for Systems at the Conference on Neural Information Processing Systems 33rd NeurIPS 2019, December 8-14

First Page

1

Last Page

10

City or Country

Vancouver

Copyright Owner and License

Authors

Additional URL

https://mlforsystems.org/assets/papers/neurips2019/treecaps_jayasundara_2019.pdf

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