Abstract
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.
Abstract (translated)
最近的人工智能（AI）特别是机器学习（ML）的进步，对软件工程研究产生了显著影响，其中包括错误报告分析。机器学习的目标是自动理解、提取并关联错误报告中的信息。尽管其重要性日益增加，但在这一领域还没有进行过全面的回顾。在这篇论文中，我们呈现了一份系统文献综述，涵盖了1,825篇文献，并从中选取了204篇进行详细分析。我们得出了七个关键发现： 1. **广泛使用CNN、LSTM和$k$NN**：这些模型被大量用于错误报告的分析，而像BERT这样的先进模型因复杂性原因未得到充分利用。 2. **Word2Vec和TF-IDF流行于特征表示**：这两种方法常用于表示文本特征，并且近年来深度学习的方法越来越受欢迎。 3. **停止词移除是最常用的预处理步骤**：然而，在2020年后，结构化方法的使用率有所上升。 4. **Eclipse和Mozilla是被评估最多的软件项目**：这两个项目在错误报告分析研究中最为常见。 5. **错误分类是最常见的任务**：其次是错误定位（bug localization）和严重性预测（severity prediction）。 6. **越来越关注特定类型的错误**：例如非功能性问题和性能相关的问题，这些领域的研究也在增加。 7. **常用评估指标包括F1-score、召回率、准确率以及精度**：在模型评估中，$k$-折交叉验证尤为受欢迎。 8. **许多研究缺乏稳健的统计测试**：这是需要进一步改进的地方。 此外，我们还确定了未来可能的研究方向有六个方面，这些方向为实践者提供了有价值的见解。
URL
https://arxiv.org/abs/2507.04422
