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

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 prioritize their work. Several studies of this problem have been conducted during 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 thesis, a novel approach using Hidden Markov Models and temporal sequences of developer activities is introduced. The approach is empirically demonstrated using eight years of bug reports collected from the Firefox project. The model correctly identifies bug reports with expected bug fix times. The approach is also compared against the frequency based classification approaches. The results indicate around 10% higher accuracy .