使用社区问答信息推荐缺陷修复者的方法
首发时间:2018-02-18
摘要:在大型开源软件项目中,准确的将缺陷报告指派给最合适的修复者进行修复是一项非常耗时的任务,因此提出一种有效的修复者推荐方法非常有必要。大多数关于修复者推荐方法的研究采用机器学习或信息检索等方法来推荐修复者,这些方法较为复杂且过分依赖修复者已有的修复数据。本文提出了一种更加有效地利用社区问答平台(如Stack Overflow)的问答信息来衡量修复者专业能力,并结合其修复工作时效性进而推荐修复者的方法。实验结果表明,本文提出的方法比大多数现行的修复者推荐方法准确率更高。
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A Method for Recommending Bug Fixer Using Community Q&A Information
Abstract: It is a very time-consuming task to assign a bug report to the most suitable fixer in large open source software projects. Therefore, it is very necessary to propose an effective recommendation method for bug fixer. Most research in this area use machine learning or information retrieval methods to recommend the bug fixer. These methods are complex and over-dependent on the fixers\' prior bug-fixing activities. In this paper, we propose a more effective bug fixer recommendation method which uses the community Q & A platforms (such as Stack Overflow) to measure the fixers\' expertise and uses the fixed bug to measure the time-aware of fixers\' fixed work. The experimental results show that the proposed method is more accurate than most of current restoration methods.
Keywords: Software Testing Software Bugs Community Q & A Fixer Recommendationl
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