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 translate it into a text classification problem and use machine learning or information retrieval methods to recommend the bug fixer. These methods are complex and overdependent 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 bugs 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.