On the use of hidden Markov model to predict the time to fix bugs

Abstract

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 (HMMs) 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. We provide additional details below. In a software bug repository, recorded developer activities occur sequentially. For example, activity C (a certain person has been copied on the bug report) is followed by activity A (bug confirmed and assigned to a named developer), which in turn is followed by activity Z (bug reached status resolved). Additional piece of information is developers' level of expertise, such as novice (N), intermediate (M), or experienced (E), at the time of report creation. We combine these data together to produce a sequence of temporal activities associated with bug reports in the Firefox bug repository.