在Bug报告的相关文件本地化中使用分布式单词表示

2016 IEEE International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2016-08-01 DOI:10.1109/QRS.2016.30

Yukiya Uneno, O. Mizuno, Eun-Hye Choi

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引用次数: 15

摘要

一旦报告了软件中的错误，开发人员必须确定哪些源文件与该错误相关。这个过程被称为错误定位，错误定位的自动方法对于提高开发人员的生产力非常重要。本文提出了一种称为DrewBL的方法，使用自然语言处理工具word2vec有效地定位给定bug报告的错误文件。在DrewBL中，我们首先建立了一个名为semantic-VSM的向量空间模型，该模型表示bug报告和源代码文件中单词的分布式表示，然后通过将构建的模型提供给word2vec来计算它们之间的相关性。为了进一步提高bug定位的准确性，我们提出了一种名为CombBL的方法，该方法不仅采用了DrewBL方法，还结合了现有的bug定位技术，如基于文本相似度的BugLocator和基于bug修复历史的Bugspots。本文给出了我们使用两个开源项目的早期实验结果，以显示所提出方法的有效性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Using a Distributed Representation of Words in Localizing Relevant Files for Bug Reports

Once a bug in software is reported, developers have to determine which source files are related to the bug. This process is referred to as bug localization, and an automatic way of bug localization is important to improve developers' productivity. This paper proposes an approach called DrewBL to efficiently localize faulty files for a given bug report using a natural language processing tool, word2vec. In DrewBL, we first build a vector space model named semantic-VSM which represents a distributed representation of words in the bug report and source code files and next compute the relevance between them by feeding the constructed model to word2vec. We also present an approach called CombBL to further improve the accuracy of bug localization which employs not only the proposed DrewBL but also existing bug localization techniques, such as BugLocator based on textual similarity and Bugspots based on bug-fixing history, in a combinational manner. This paper gives our early experimental results to show the effectiveness and efficiency of the proposed approaches using two open source projects.

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2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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