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Combining Word Embedding with Information Retrieval to Recommend Similar Bug Reports

机译：结合词嵌入和信息检索来推荐类似的错误报告

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摘要

Similar bugs are bugs that require handling of many common code files. Developers can often fix similar bugs with a shorter time and a higher quality since they can focus on fewer code files. Therefore, similar bug recommendation is a meaningful task which can improve development efficiency. Rocha et al. propose the first similar bug recommendation system named NextBug. Although NextBug performs better than a start-of-the-art duplicated bug detection technique REP, its performance is not optimal and thus more work is needed to improve its effectiveness. Technically, it is also rather simple as it relies only upon a standard information retrieval technique, i.e., cosine similarity. In the paper, we propose a novel approach to recommend similar bugs. The approach combines a traditional information retrieval technique and a word embedding technique, and takes bug titles and descriptions as well as bug product and component information into consideration. To evaluate the approach, we use datasets from two popular open-source projects, i.e., Eclipse and Mozilla, each of which contains bug reports whose bug ids range from [1,400000]. The results show that our approach improves the performance of NextBug statistically significantly and substantially for both projects.
机译：类似的错误是需要处理许多常见代码文件的错误。由于他们可以专注于更少的代码文件，因此开发人员通常可以用更短的时间和更高的质量来修复类似的错误。因此，类似的错误建议是一项有意义的任务，可以提高开发效率。 Rocha等。提出第一个类似的Bug推荐系统NextBug。尽管NextBug的性能优于最新的重复错误检测技术REP，但它的性能并不是最佳的，因此需要更多的工作来提高其有效性。从技术上讲，它也很简单，因为它仅依赖于标准的信息检索技术，即余弦相似度。在本文中，我们提出了一种新颖的方法来推荐类似的错误。该方法结合了传统的信息检索技术和词嵌入技术，并考虑了错误标题和描述以及错误产品和组件信息。为了评估该方法，我们使用来自两个流行的开源项目的数据集，即Eclipse和Mozilla，每个项目都包含错误报告，其错误ID范围为[1,400000]。结果表明，对于两个项目，我们的方法均可以显着且显着地提高NextBug的性能。

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