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Code quality control by bug report classification

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Abstract

Software maintenance phase involves successive code changes due to the reported bugs causing the emergence of bad smells in the code. Gradual code deterioration is the result of such practice and brings about hard to maintain code by affecting the code quality adversely. The purpose is to alert the project manager to the presumptive quality critical bugs (QCB) as soon as the bug reports are recorded in the issue tracking system (ITS) and help treat them more carefully by assigning those to the more experienced developers to be fixed or by prioritizing those QCBs in the quality control list. One-phased and two-phased bug classification models are proposed in this paper. The former makes the predictions using a convolutional neural network and the latter is based on the bug localization algorithms (as the first step) and time series prediction techniques (as the second step). Using four large-size open-source Apache products, it was observed that the two-phased approach could reach 78% prediction accuracy. The two-phased method was able to identify unpredictable bug reports by analyzing the time series of its target classes. The two-phased method outperformed the one-phased method in products involving highly predictable classes’ time series. In contrast, the simpler one-phased method performed comparable to the two-phased method in low-predictable products. Code quality control (CQC) is a vital task over bug-fixing process to delay the code deterioration by refactoring activities. Bug classification techniques have already been applied in the bug severity prediction and bug triaging areas but not in the CQC applications.

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The data will be available on demand.

Notes

  1. available at: https://www.bugzilla.org (accessed 06 June 2018).

  2. available at: https://www.atlassian.com/software/jira?bundle=jira-software&edition=free (accessed 06 June 2018).

  3. available at: https://github.com/Ziba-Ghane/CQC.git (accessed 09 June 2022).

  4. available at: https://code.google.com/archive/p/word2vec/ (accessed 08 June 2022).

  5. available at: https://pmd.github.io/latest/pmd_rules_java.html (accessed 07 June 2022).

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran

    Omid Bushehrian & Ziba Ghane

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  1. Omid Bushehrian
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  2. Ziba Ghane
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Contributions

The first author conducted the research methodology and wrote the manuscript. The second author prepared the figures and tables and did the implementations. Both authors worked on the experimental results and reviewed the manuscript.

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Correspondence to Omid Bushehrian.

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Bushehrian, O., Ghane, Z. Code quality control by bug report classification. Software Qual J 31, 991–1007 (2023). https://doi.org/10.1007/s11219-023-09623-7

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