Visheshagya: Time based expertise model for bug report assignment

The brisk escalation in scale of software systems has made bug triaging an imperative step in bug fixing process. A huge amount of bug reports is submitted daily to bug tracking repositories. Although this practice assists in building a reliable and error-free software product but handling a large amount of work becomes challenging. Bug assignment, an essential step in bug triaging, is the process of designating a suitable developer for the bug report who could make code changes in order to fix the bug. Various approaches ranging from semi to fully automatic bug assignment are proposed in literature. These approaches are mostly based on machine learning and information retrieval techniques. Since the information retrieval based activity profiling approach achieves higher accuracy, they are more often used in recent studies. Time factor based normalization in activity profiling could play a vital role in analyzing the level of expertise (or knowledge) of developers as the knowledge decays with time. This paper proposes a time oriented expertise model, Visheshagya, which utilizes the meta-fields of bug reports for developer selection. The proposed technique is used to prioritize the developers actively participating in software bug repository on the basis of their current knowledge. The proposed approach has been validated on two popular projects of Bugzilla repository, Mozilla and Eclipse. The result shows that time based activity profiling of developers outperforms existing information retrieval based bug report assignment and achieves an improvement of 14.3% and 9.95% in the accuracy of top-10 list size in Mozilla and Eclipse projects respectively.