跨语言bug定位

Xin Xia, D. Lo, Xingen Wang, Chenyi Zhang, Xinyu Wang
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引用次数: 32

摘要

Bug本地化是指从Bug报告中的文本描述中识别包含缺陷的源代码文件的过程。现有的bug本地化技术是基于这样一个假设:bug报告、源代码文件中的标识符和注释都是用同一种语言(即英语)编写的。然而,来自非英语国家(例如,中国)的软件用户经常使用他们的母语(例如,中文)来编写错误报告。对于这种设置,现有的关于bug本地化的研究将不起作用,因为bug报告中出现的术语不会出现在源代码中。我们把这个问题称为跨语言bug本地化。本文提出了一种基于语言翻译的跨语言bug定位算法——CrosLocator。由于不同的在线翻译器(例如谷歌和Microsoft翻译器)对各种文本的翻译精度不同,CrosLocator使用多个翻译器将错误报告的非英语文本描述转换为英语-每个错误报告将有多个翻译版本。对于每个翻译版本,CrosLocator应用错误定位技术对源代码文件进行排序。最后,CrosLocator组合源代码文件的多个排序列表。我们在Ruby-China上的初步实验表明,CrosLocator可以实现平均倒数秩(mrr)和平均平均精度(map)分数高达0.146和0.116,分别比基线方法平均高出10%和12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-language bug localization
Bug localization refers to the process of identifying source code files that contain defects from textual descriptions in bug reports. Existing bug localization techniques work on the assumption that bug reports, and identifiers and comments in source code files, are written in the same language (i.e., English). However, software users from non-English speaking countries (e.g., China) often use their native languages (e.g., Chinese) to write bug reports. For this setting, existing studies on bug localization would not work as the terms that appear in the bug reports do not appear in the source code. We refer to this problem as cross-language bug localization. In this paper, we propose a cross-language bug localization algorithm named CrosLocator, which is based on language translation. Since different online translators (e.g., Google and Microsoft translators) have different translation accuracies for various texts, CrosLocator uses multiple translators to convert a non-English textual description of a bug report into English -- each bug report would then have multiple translated versions. For each translated version, CrosLocator applies a bug localization technique to rank source code files. Finally, CrosLocator combines the multiple ranked lists of source code files. Our preliminary experiment on Ruby-China shows that CrosLocator could achieve mean reciprocal rank (mrr) and mean average precision (map) scores of up to 0.146 and 0.116, which outperforms a baseline approach by an average of 10% and 12% respectively.
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