LAUSR

A significant amount of time is spent by software developers in investigating bug reports. It is useful to indicate when a bug report will be closed, since it would help software teams to prioritise their work. Several studies have been conducted to address this problem in the past decade. Most of these studies have used the frequency of occurrence of certain developer activities as input attributes in building their prediction models. However, these approaches tend to ignore the temporal nature of the occurrence of these activities. In this paper, a novel approach using Hidden Markov Models and temporal sequences of developer activities is proposed. The approach is empirically demonstrated in a case study using eight years of bug reports collected from the Firefox project. Our proposed model correctly identifies bug reports with expected bug fix times. We also compared our proposed approach with the state of the art technique in the literature in the context of our case study. Our approach results in approximately 33 percent higher F-measure than the contemporary technique based on the Firefox project data.