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A bug you like: A framework for automated assignment of bugs

机译:您喜欢的错误:自动分配错误的框架

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Assigning bug reports to individual developers is typically a manual, time-consuming, and tedious task. In this paper, we present a framework for automated assignment of bug-fixing tasks. Our approach employs preference elicitation to learn developer predilections in fixing bugs within a given system. This approach infers knowledge about a developer's expertise by analyzing the history of bugs previously resolved by the developer. We apply a vector space model to recommend experts for resolving bugs. When a new bug report arrives, the system automatically assigns it to the appropriate developer considering his or her expertise, current workload, and preferences. We address the task allocation problem by proposing a set of heuristics that support accurate assignment of bug reports to the developers.
机译:将错误报告分配给各个开发人员通常是一项手动,耗时且繁琐的任务。在本文中,我们提出了一个自动分配错误修复任务的框架。我们的方法利用偏好启发来学习开发人员在解决给定系统中的错误方面的偏好。这种方法通过分析以前由开发人员解决的错误的历史来推断有关开发人员的专业知识。我们应用向量空间模型来推荐专家来解决错误。当收到新的错误报告时,系统会根据其专业知识,当前工作量和偏好将其自动分配给适当的开发人员。我们通过提出一组启发式方法来解决任务分配问题,这些启发式方法可将错误报告准确地分配给开发人员。

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