• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Software Maintenance and Evolution >Are Bug Reports Enough for Text Retrieval-Based Bug Localization?
【24h】

Are Bug Reports Enough for Text Retrieval-Based Bug Localization?

机译：错误报告足以用于基于文本检索的错误本地化？

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Text Retrieval (TR) has been widely used to support many software engineering tasks, including bug localization (i.e., the activity of localizing buggy code starting from a bug report). Many studies show TR's effectiveness in lowering the manual effort required to perform this maintenance task; however, the actual usefulness of TR-based bug localization has been questioned in recent studies. These studies discuss (i) potential biases in the experimental design usually adopted to evaluate TRbased bug localization techniques and (ii) their poor performance in the scenario when they are needed most: when the bug report, which serves as the de facto query in most studies, does not contain localization hints (e.g., code snippets, method names, etc.) Fundamentally, these studies raise the question: do bug reports provide sufficient information to perform TR-based localization? In this work, we approach that question from two perspectives. First, we investigate potential biases in the evaluation of TR-based approaches which artificially boost the performance of these techniques, making them appear more successful than they are. Second, we analyze bug report text with and without localization hints using a genetic algorithm to derive a near-optimal query that provides insight into the potential of that bug report for use in TR-based localization. Through this analysis we show that in most cases the bug report vocabulary (i.e., the terms contained in the bug title and description) is all we need to formulate effective queries, making TR-based bug localization successful without supplementary query expansion. Most notably, this also holds when localization hints are completely absent from the bug report. In fact, our results suggest that the next major step in improving TR-based bug localization is the ability to formulate these near-optimal queries.
机译：文本检索（TR）已被广泛用于支持许多软件工程任务，包括错误本地化（即，从错误报告开始定位错误代码的活动）。许多研究表明TR在降低执行此维护任务所需的手动努力方面的有效性;然而，在最近的研究中，基于TR基本错误定位的实际有用性受到质疑。这些研究讨论（i）实验设计中的潜在偏见通常采用来评估TRBASed Bug定位技术和（ii）当您最需要的情况下它们在方案中的性能很差：当错误报告时，它是最重要的查询研究，不包含本地化提示（例如，代码片段，方法名称等），这些研究提出了问题：DO错误报告提供足够的信息来执行基于TR的本地化吗？在这项工作中，我们从两个角度来看这个问题。首先，我们调查评估TR基方法中的潜在偏见，这是人为地提高了这些技术的性能，使它们看起来比它们更成功。其次，我们使用遗传算法分析错误报告文本，并使用遗传算法导出近最优查询，为该错误报告提供了洞察，以便在基于TR的本地化中使用。通过此分析，我们显示在大多数情况下，错误报告词汇（即，错误标题和描述中包含的术语）是我们需要制定有效查询，使基于TR的BUG本地化成功，没有补充查询扩展。最值得注意的是，当来自错误报告完全没有本地化提示时，这也持有。事实上，我们的结果表明，改善基于TR的错误本地化的下一个重大步骤是能够制定这些近最佳查询。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号