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Path context augmented statement and network for learning programs

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Abstract

Applying machine learning techniques in program analysis has attracted much attention. Recent research efforts in detecting code clones and classifying code have shown that neural models based on abstract syntax trees (ASTs) can better represent source code than other approaches. However, existing AST-based approaches do not take into account contextual information of a program, like statement context. To address this issue, we propose a novel approach path context to capture the context of statements, and a path context augmented network (PCAN) to learn a program. We evaluate PCAN on code clone detection, source code classification, and method naming. The results show that compared to state-of-the-art approaches, PCAN performs the best on code clone detection and has comparable performance on code classification and method naming.

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Notes

  1. https://docs.oracle.com/javase/tutorial/java/nutsandbolts/expressions.html

  2. pyparser homepage, https://pypi.python.org/pypi/pycparser

  3. javaparser homepage, https://github.com/javaparser/javaparser

  4. We built our dataset by following the data preparation method in CDLH (Wei and Li , 2017) so that we could directly use and compare with their results.

  5. Astminer: https://github.com/JetBrains-Research/astminer

  6. https://github.com/HongliangLiang/pcan/

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Authors and Affiliations

  1. Beijing University of Posts and Communications, Beijing, China

    Da Xiao, Dengji Hang, Lu Ai & Hongliang Liang

  2. Colorful Clouds Technology Co., Ltd., Beijing, China

    Shengping Li

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  1. Da Xiao
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Communicated by Foutse Khomh, Gemma Catolino and Pasquale Salza.

This article belongs to the Topical Collection: Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)

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Xiao, D., Hang, D., Ai, L. et al. Path context augmented statement and network for learning programs. Empir Software Eng 27, 37 (2022). https://doi.org/10.1007/s10664-021-10098-y

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