支持Bug修复的相关Bug修复评论的摘录摘要

Rrezarta Krasniqi
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引用次数: 6

摘要

当开发人员调查一个新的错误报告时,他们会搜索类似的先前修复的错误报告以及与之相关的讨论线程。这些讨论线程传递有关bug行为的重要信息,包括相关的bug修复注释。通常,由于报告的错误的严重性,这些讨论线程会变得非常冗长。这增加了另一层复杂性,特别是当相关的bug修复注释与看似不相关的注释混杂在一起时。在处理大量的bug报告时,在各种横切讨论线程中手动检测这些相关的注释可能会成为一项艰巨的任务。为了使这个过程自动化,我们的重点是在查询相关性、积极语言的使用和语义相关性的上下文中提取和检测评论。然后,为了便于理解,我们以摘要的形式合并这些注释。具体来说,我们将情感分析和TextRank模型与基线向量空间模型(VSM)结合起来。初步发现表明,bug修复评论往往是积极的,并且与来自其他横切讨论线程的评论存在语义相关性。结果还表明,我们的组合方法提高了相对于基线VSM的整体排名性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extractive Summarization of Related Bug-fixing Comments in Support of Bug Repair
When developers investigate a new bug report, they search for similar previously fixed bug reports and discussion threads attached to them. These discussion threads convey important information about the behavior of the bug including relevant bug-fixing comments. Often times, these discussion threads become extensively lengthy due to the severity of the reported bug. This adds another layer of complexity, especially if relevant bug-fixing comments intermingle with seemingly unrelated comments. To manually detect these relevant comments among various cross-cutting discussion threads can become a daunting task when dealing with high volume of bug reports. To automate this process, our focus is to initially extract and detect comments in the context of query relevance, the use of positive language, and semantic relevance. Then, we merge these comments in the form of a summary for easy understanding. Specifically, we combine Sentiment Analysis, and the TextRank Model with the baseline Vector Space Model (VSM). Preliminary findings indicate that bug-fixing comments tend to be positive and there exists a semantic relevance with comments from other cross-cutting discussion threads. The results also indicate that our combined approach improves overall ranking performance against the baseline VSM.
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