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Modeling source code in bimodal for program comprehension

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Abstract

Source code is an intermediary through which humans communicate with computer systems. It contains a large amount of domain knowledge which can be learned by statistical models. Furthermore, this knowledge can be used to build software engineering tools. We find that the functionality of the source code depends on the programming language-specific token which build the base structure, while identifiers provide natural language information. On this basis, we found that the knowledge in the source code can be sufficiently learned more when modeling the source code in bimodal. This paper presents the bimodal composition language model (BCLM) for source code modeling and representation. We analyze the effectiveness of bimodal modeling, and the results show that the bimodal approach has great potential for source code modeling and program comprehension.

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Data availability

This article uses the publicly available dataset CodeSearchNet, which can be accessed by following link: https://github.com/github/CodeSearchNet. Apart from this, no private dataset is used as evaluation data in this paper.

Notes

  1. https://nlp.stanford.edu/projects/glove/

  2. https://golang.org/ref/spec.

  3. https://www.php.net/manual/en/reserved.php

  4. https://docs.oracle.com/javase/tutorial/java/nutsandbolts/_keywords.html.

  5. https://docs.python.org/3/reference/lexical_analysis.html.

  6. https://ruby-doc.org/docs/ruby-doc-bundle/Manual/man0-1.4/syntax.html.

  7. https://developer.mozilla.org/en-US/docs/Web/JavaScript/Language_Resources.

  8. https://nlp.stanford.edu/projects/glove/

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Funding

This work is partially supported by grant from the Natural Science Foundation of China (No.62076046 and No.62006130), Inner Monoglia Science Foundation (No.2022MS06028). This work is supported by the National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian. This work is also supported by the Inner Mongolia Directly College and University Scientific Basic in 2022.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, 2rd Ling Gong Road, Dalian, 116024, Liaoning, China

    Dongzhen Wen, Xiaokun Zhang, Ziyun Zhao & Hongfei Lin

  2. Department of Software Engineering, School of Software Technology, Dalian University of Technology, No. 321, Tuqiang Street, Dalian Economic and Technological Development Zone, Dalian, 116024, Liaoning, China

    He Jiang

  3. School of Computer Science and Technology, Inner Mongolia University for Nationalities, 536 West Huolinhe Street, Tongliao, 028000, Inner Mongolia, China

    Yufeng Diao

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Wen, D., Zhang, X., Diao, Y. et al. Modeling source code in bimodal for program comprehension. Neural Comput & Applic 36, 13815–13832 (2024). https://doi.org/10.1007/s00521-024-09498-0

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