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Production and test bug report classification based on transfer learning
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AI
DOI：10.1016/j.infsof.2025.107685.png)
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摘要
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Context: Recent studies indicate that the classification of production and test bug reports can substantially enhance the accuracy of performance evaluation and the effectiveness of information retrieval-based bug localization (IRBL) for software reliability. Objective: However, manually classifying these bug reports is time-consuming for developers. This study introduces a production and test bug report classification (ProTeC) framework for automatically classifying these reports. Methods: The framework's novelty lies in leveraging a set of production- and test-source files and employing transfer learning to address the issue of insufficient and sparse bug reports in machine-learning applications. The ProTeC framework trains and fine-tunes a source file classifier to develop a bug report classifier by transferring production-test distinguishing knowledge. Results: To validate the effectiveness and general practicality of ProTeC, we conducted large-scale experiments using 2,522 bug reports across 12 machine/deep learning model variations to train an automatic classifier. Our results, on average, demonstrate that ProTeC's macro F1-score is 28.6% higher than that of a bug report-based classifier, and it can improve the mean average precision of IRBL by 17.6%. Conclusion: These positive trends were observed in most model variations, indicating that ProTeC consistently performs well in classifying bug reports regardless of the model used, thereby improving IRBL performance.
Keyword:
Empirical study
Software reliability
Information retrieval-based bug localization
Bug report classification
Transfer learning
期刊
IF:
4.3
论文数:
3.8K
被引数:
7.7K
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Does Deep Learning improve the performance of duplicate bug report detection? An empirical study?深度学习是否提高了重复错误报告检测的性能？实证研究？
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