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In software testing and maintenance activities, the observed faults and bugs are reported in bug report managing systems (BRMS) for further analysis and repair. According to the information provided by bug reports, developers need to find out the location of these faults and fix them. However, bug locating usually involves intensively browsing back and forth through bug reports and software code and thus incurs unpredictable cost of labor and time. Hence, establishing a robust model to efficiently and effectively locate and track faults is crucial to facilitate software testing and maintenance. In our observation, some related bug locations are tightly associated with the implicit links among source files. In this paper, we present an implicit social network model using PageRank to establish a social network graph with the extracted links. When a new bug report arrives, the prediction model provides users with likely bug locations according to the implicit social network graph constructed from the co-cited source files. The proposed approach has been implemented in real-world software archives and can effectively predict correct bug locations.
Chen et al. (Tue,) studied this question.
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