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.
Highlights
In the process of software development, bugs are unavoidable and it is important to fix bugs in time
Bug triager assign the bug to a list of developers that are qualified to understand and fix the bug report, and ranking them according to their expertise[1]
The bug reports used in this experiment have been assigned to a member of the project, that is, every bug report corresponds to a real bug fixer
Summary
In the process of software development, bugs are unavoidable and it is important to fix bugs in time. Jeong et al [8], Bhattacharya et al [9] built a bug tossing graph using fixed bugs’ tossing information, updated the recommendation list using the tossing graph to recommend a better potential fixer These methods have an obsolete training set problem and the fixer recommended must fixed some bug reports similar to the newly one. Sajedi et al [12] proposed a method named RA _ SSA _ Z _ scoreu,b using question-answering (Q&A) information in Stack Overflow platform as evidence for the fixers’ expertise to recommend fixer. Based on the research of Sajedi et al, this paper proposes a more optimized and efficient fixer recommendation method, which further studies how to use the information of CQA platform to measure the expertise of developers and use the fixed bugs to measure the timeliness of candidate fixers’ fixed work
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