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bjXnet: an improved bug localization model based on code property graph and attention mechanism

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Abstract

Bug localization technologies and tools are widely used in software engineering. Although state-of-the-art methods have achieved great progress, they only consider the source code information at the text level, which may establish a wrong correlation between the source code and the bug report, affecting the localization accuracy and reliability. In this paper, we propose an improved bug localization model, which uses the semantics of source codes at the graph level to supplement its semantics at the text level, optimizing and adjusting the graph semantics in combination with the attention mechanism to obtain the code semantic feature including the shallow and deep semantics of the source code. Finally, the correlation between code semantic feature and report semantic feature is measured by cosine similarity. We conduct experiments on three open source Java projects to comprehensively evaluate the performance of proposed model. The experimental results show that the model is significantly better than state-of-the-art methods.

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Notes

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

  2. https://github.com/joernio/joern.

  3. https://www.dgl.ai.

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Acknowledgements

This paper is supported by the National Key Research and Development Program of China (No.2021YFB3100500) and the Frontier Science and Technology Innovation Projects of National Key Research and Development Program (No.2019QY1405).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Sichuan University, Chengdu, 610207, Sichuan, China

    Jiaxuan Han, Cheng Huang, Siqi Sun, Zhonglin Liu & Jiayong Liu

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  1. Jiaxuan Han
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  2. Cheng Huang
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  3. Siqi Sun
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  4. Zhonglin Liu
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  5. Jiayong Liu
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Contributions

JH and CH conceived the proposed model. JH and SS realized the proposed model by programming. ZL constructed the experiment and evaluated the proposed model comprehensively on the experiment dataset. JH and CH wrote the manuscript text, and all authors reviewed it.

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Correspondence to Cheng Huang.

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Han, J., Huang, C., Sun, S. et al. bjXnet: an improved bug localization model based on code property graph and attention mechanism. Autom Softw Eng 30, 12 (2023). https://doi.org/10.1007/s10515-023-00379-9

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