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Supplementary Bug Fixes vs. Re-opened Bugs

机译:补充错误修复与重新打开的错误

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A typical bug fixing cycle involves the reporting of a bug, the triaging of the report, the production and verification of a fix, and the closing of the bug. However, previous work has studied two phenomena where more than one fix are associated with the same bug report. The first one is the case where developers re-open a previously fixed bug in the bug repository (sometimes even multiple times) to provide a new bug fix that replace a previous fix, whereas the second one is the case where multiple commits in the version control system contribute to the same bug report ("supplementary bug fixes"). Even though both phenomena seem related, they have never been studied together, i.e., are supplementary fixes a subset of re-opened bugs or the other way around? This paper investigates the interplay between both phenomena in five open source software projects: Mozilla, Net beans, Eclipse JDT Core, Eclipse Platform SWT, and Web Kit. We found that re-opened bugs account for between 21.6% and 33.8% of all supplementary fixes. However, 33% to 57.5% of re-opened bugs had only one commit associated, which means that the original bug report was prematurely closed instead of fixed incorrectly. Furthermore, we constructed predictive models for re-opened bugs using historical information about supplementary bug fixes with a precision between 72.2% and 97%, as well as a recall between 47.7% and 65.3%. Software researchers and practitioners who are mining data repositories can use our approach to identify potential failures of their bug fixes and the re-opening of bug reports.
机译:典型的错误修复周期涉及错误报告,报告分类,修复的产生和验证以及错误的关闭。但是,以前的工作研究了两种现象,其中同一修补程序错误报告与多个修复程序相关联。第一种情况是开发人员在错误存储库中重新打开以前修复的错误(有时甚至多次)以提供新的错误修复程序来替换先前的修复程序,而第二种情况是在版本中多次提交控制系统贡献相同的错误报告(“补充错误修复”)。即使两种现象似乎都相关,但从未一起研究过它们,即补充性修复程序是重新打开的bug的子集还是相反的方式?本文研究了五个开源软件项目中这两种现象之间的相互作用:Mozilla,Net bean,Eclipse JDT Core,Eclipse Platform SWT和Web Kit。我们发现重新打开的错误占所有补充修补程序的21.6%至33.8%。但是,33%到57.5%的重新打开的错误只关联了一次提交,这意味着原始错误报告被过早关闭而不是错误地修复。此外,我们使用有关补充性错误修复的历史信息为重新打开的错误构建了预测模型,其准确性介于72.2%和97%之间,召回率介于47.7%和65.3%之间。挖掘数据存储库的软件研究人员和从业人员可以使用我们的方法来确定其错误修复和重新打开错误报告的潜在失败。

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