RAPTOR: Release-Aware and Prioritized Bug-Fixing Task Assignment Optimization
RAPTOR: Release-Aware and Prioritized Bug-Fixing Task Assignment Optimization
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DOI：
10.1109/icsme.2019.00101
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Yutaro Kashiwa
中科院分区：
文献类型：
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作者：
Yutaro Kashiwa
十多年来，已经提出了许多错误分配方法，以帮助开发人员阅读每天提交的大量错误报告，并分配合适的开发人员。然而，他们倾向于将任务集中在少数特定的开发人员身上。将这些方法应用于已经发布的项目将减少开发人员在下一个发布日期之前可以修复的错误数量，因为开发人员可以投入到修复错误的时间是有限的。在这项研究中，我们提出了版本感知的错误修复任务分配方法，以缓解任务集中度，增加开发人员在下一个发布日期之前可以修复的错误数量。该方法使用数学规划在项目级别找到最佳组合，而传统方法则在个人级别找到错误和开发人员的最佳组合。
Over a decade, many bug assignment methods have been proposed in order to assist developers to read bug reports submitted daily and numerously, and to assign an appropriate developer. However, they tend to concentrate their assignments on a small number of particular developers. Applying the methods to the projects which have releases would reduce the number of bugs that developers can fix by the next release date because the time that developers can devote to bug-fixing is limited. In this study, we propose the release-aware bug-fixing task assignment method to mitigate the task concentration and increase the number of bugs that developers can fix by the next release date. This method employs mathematical programming to find the best combination at project level while the traditional methods find the best pair of a bug and a developer (at individual level).