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Enhancing supervised bug localization with metadata and stack-trace

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Abstract

Locating relevant source files for a given bug report is an important task in software development and maintenance. To make the locating process easier, information retrieval methods have been widely used to compute the content similarities between bug reports and source files. In addition to content similarities, various other sources of information such as the metadata and the stack-trace in the bug report can be used to enhance the localization accuracy. In this paper, we propose a supervised topic modeling approach for automatically locating the relevant source files of a bug report. In our approach, we take into account the following five key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. Fourth, metainformation brings additional guidance on the search space. Fifth, buggy source files could be already contained in the stack-trace. By integrating the above five observations, we experimentally show that the proposed method can achieve up to 67.1% improvement in terms of prediction accuracy over its best competitors and scales linearly with the size of the data.

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Notes

  1. This work is an extended version of our previous work [3] which considers the previous three components. Please refer to the related work section for more details.

  2. In this paper, we interchangeably use ‘document’ and ‘bug report.’

  3. A bug report may relate to multiple source files.

  4. To simplify the processing of source files, we only keep the words in source flies that have appeared in the bug reports.

  5. We incorporate these four terms in the model for completeness

  6. https://bugs.eclipse.org/.

  7. http://git.eclipse.org/, https://github.com/eclipse/.

  8. This is exactly the STMLocator method in the previous conference version [3].

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61690204, 61672274, 61702252) and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Hanghang Tong is partially supported by NSF (1651203, 1715385, and 1939725).

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Authors and Affiliations

  1. The State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China

    Yaojing Wang, Yuan Yao, Xuan Huo, Ming Li, Feng Xu & Jian Lu

  2. University of Illinois Urbana-Champaign, Champaign, USA

    Hanghang Tong

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  1. Yaojing Wang
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  2. Yuan Yao
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  3. Hanghang Tong
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  4. Xuan Huo
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Correspondence to Yaojing Wang.

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Hanghang Tong: The work was partly done, while the author was at Arizona State University.

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Wang, Y., Yao, Y., Tong, H. et al. Enhancing supervised bug localization with metadata and stack-trace. Knowl Inf Syst 62, 2461–2484 (2020). https://doi.org/10.1007/s10115-019-01426-2

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