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

IF 2 2区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Automated Software Engineering Pub Date : 2023-03-07 DOI:10.1007/s10515-023-00379-9

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

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引用次数: 4

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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bjXnet:改进的基于代码属性图和关注机制的bug定位模型

Bug定位技术和工具在软件工程中得到了广泛应用。虽然目前的方法已经取得了很大的进步，但它们只在文本层面考虑源代码信息，这可能会在源代码和bug报告之间建立错误的关联，影响定位的准确性和可靠性。本文提出了一种改进的bug定位模型，该模型利用源代码在图层的语义来补充其在文本层的语义，并结合注意机制对图语义进行优化和调整，从而获得包括源代码的浅语义和深语义在内的代码语义特征。最后，用余弦相似度度量代码语义特征与报表语义特征之间的相关性。我们在三个开源Java项目上进行了实验，以全面评估所提出模型的性能。实验结果表明，该模型明显优于现有的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Automated Software Engineering 工程技术-计算机：软件工程

CiteScore

4.80

自引率

11.80%

发文量

审稿时长

>12 weeks

期刊介绍： This journal details research, tutorial papers, survey and accounts of significant industrial experience in the foundations, techniques, tools and applications of automated software engineering technology. This includes the study of techniques for constructing, understanding, adapting, and modeling software artifacts and processes. Coverage in Automated Software Engineering examines both automatic systems and collaborative systems as well as computational models of human software engineering activities. In addition, it presents knowledge representations and artificial intelligence techniques applicable to automated software engineering, and formal techniques that support or provide theoretical foundations. The journal also includes reviews of books, software, conferences and workshops.