Bug reports are an essential aspect of software development, and it is crucial to identify and resolve them quickly to ensure the consistent functioning of software systems. Retrieving similar bug reports from an existing database can help reduce the time and effort required to resolve bugs. In this paper, we compared the effectiveness of semantic textual similarity methods for retrieving similar bug reports based on a similarity score. We explored several embedding models such as TF-IDF (Baseline), FastText, Gensim, BERT, and ADA. We used the Software Defects Data containing bug reports for various software projects to evaluate the performance of these models. Our experimental results showed that BERT generally outperformed the rest of the models regarding recall, followed by ADA, Gensim, FastText, and TFIDF. Our study provides insights into the effectiveness of different embedding methods for retrieving similar bug reports and highlights the impact of selecting the appropriate one for this task. Our code is available on GitHub.

翻译：错误报告是软件开发的重要组成部分，快速识别并解决它们对于确保软件系统的持续运行至关重要。从现有数据库中检索相似错误报告有助于减少解决错误所需的时间和精力。本文基于相似度得分，比较了语义文本相似度方法在检索相似错误报告时的有效性。我们探索了多种嵌入模型，如TF-IDF（基线）、FastText、Gensim、BERT和ADA。我们使用了包含多个软件项目错误报告的软件缺陷数据集来评估这些模型的性能。实验结果表明，在召回率方面，BERT普遍优于其他模型，其次是ADA、Gensim、FastText和TF-IDF。我们的研究揭示了不同嵌入方法在检索相似错误报告时的有效性，并强调了为此任务选择合适模型的重要性。我们的代码已发布在GitHub上。