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CLeBPI: Contrastive Learning for Bug Priority Inference
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DOI：10.1016/j.infsof.2023.107302.png)
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Abstract
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Context: Automated bug priority inference (BPI) can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. Objective: There are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports; 2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. Method: We propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for BPI in a supervised way. Results: We choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its effectiveness. Conclusion: This paper propose CLeBPI, a pre-trained model combining contrastive learning that can auto-matically predict bug priority. Experimental results show that It achieves new result in BPI and can effectively alleviate label imbalance problem.
Keywords:
Contrastive learning
Bug report
Bug priority inference
Software maintenance
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