Who should catch ( this Exception ) { ?

In this paper, we propose a software tool that can be used to speed up the software development lifecycle by automating the process of assigning new bugs to developers. Open bug repositories are now very common, and they are provided to the software users as a means to report any bugs they find while using the software. Many of the bugs reported complain about exceptions that are thrown by the program during normal operation of the software. Users report these bugs to the development team using bug reporting tools, (e.g. Bugzilla), and as a result of that, a person called Bug Triager tries to link between this bug and a developer who can fix it. Sometimes, the source of the bug is unclear, and as a result of that the developer can be reassigned several times. In this project, we propose an approach to automate this process by recommending developers who can fix a bug that complains from an exception. Moreover, we provide a ranking of developers according to their expected relationship with the bug so that more than one developer can work on the bug if needed, or if the first recommendation is unavailable. The approach is based on network analysis, and uses link prediction to rank developers with respect to a bug report. Moreover, in this report, in addition to using link prediction, we show several applications to using network analysis in the context of software analysis, like importance ranking and visualization. The Eclipse open source project was taken as a case study, and its CVS and Bugzilla repositories were the source of the datasets used to evaluate our approach. Experimental evaluation of the link prediction approach for recommending developers reveals its effectiveness and shows that its recommendations were similar to the actual bug assignments to a far extent.