系统梳理1825篇论文，揭示软件缺陷报告分析的现状与方向
Learning Software Bug Reports: A Systematic Literature Review
- 基于204篇精选论文，总结七项核心发现
- 深度学习模型应用仍受限，特征提取以词向量为主
- 适合研究者和开发者参考，尤其关注缺陷分类与评估方法
人工智能，尤其是机器学习（ML），已深刻影响软件工程研究，包括缺陷报告分析。尽管其重要性日益凸显，该领域尚无全面综述。本文通过系统文献回顾，涵盖1,825篇论文，筛选出204篇进行深入分析。得出七项关键发现：1）卷积神经网络（CNN）、长短期记忆网络（LSTM）和k近邻（kNN）被广泛使用，而如BERT等先进模型因复杂性未被充分采用；2）词向量（Word2Vec）和词频-逆文档频率（TF-IDF）是主流特征表示方法，深度学习方法呈上升趋势；3）停用词移除是最常见预处理方式，2020年后结构化方法逐渐增多；4）Eclipse和Mozilla是主要评估项目；5）缺陷分类是最常见任务，其次是缺陷定位与严重性预测；6）对非功能性及性能缺陷的关注度持续上升；7）常用评估指标为F1分数、召回率、精确率与准确率，多数研究采用k折交叉验证；8）许多研究缺乏严格的统计检验。此外，我们识别出六个未来有前景的研究方向，为实践者提供有益参考。
原文摘要 · Abstract (English)
The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aims to automate the understanding, extraction, and correlation of information from bug reports. Despite its growing importance, there has been no comprehensive review in this area. In this paper, we present a systematic literature review covering 1,825 papers, selecting 204 for detailed analysis. We derive seven key findings: 1) Extensive use of CNN, LSTM, and $k$NN for bug report analysis, with advanced models like BERT underutilized due to their complexity. 2) Word2Vec and TF-IDF are popular for feature representation, with a rise in deep learning approaches. 3) Stop word removal is the most common preprocessing, with structural methods rising after 2020. 4) Eclipse and Mozilla are the most frequently evaluated software projects. 5) Bug categorization is the most common task, followed by bug localization and severity prediction. 6) There is increasing attention on specific bugs like non-functional and performance bugs. 7) Common evaluation metrics are F1-score, Recall, Precision, and Accuracy, with $k$-fold cross-validation preferred for model evaluation. 8) Many studies lack robust statistical tests. We also identify six promising future research directions to provide useful insights for practitioners.
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