Abstract
Nowadays, software projects receive a huge number of bug reports daily. Among them, security and performance bug reports are higher priority to software developers and users. So, rapid identification of security and performance bug reports as soon as these are reported is mandatory. But bug tracking systems do not provide any mechanism to isolate them from the collection of bug reports. In this paper, we have proposed a learning based approach to identify security and performance bug reports addressing class-bias and feature-skew phenomenon. We have proposed two separate classification models namely Sec-Model and Perf-Model, where the former classifies a bug report as security or non-security bug report and the latter classifies as performance or non-performance bug report. We have experimented our approach on four datasets of bug reports of four software projects- Ambari, Camel, Derby and Wicket. We have evaluated the performance of our two models in terms of area under curve receiver operating characteristics curve (AUC). The average AUC values of Sec-Model and Perf-Model are 0.67 and 0.71 respectively.
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