Predicting bug-fixing time: A replication study using an open source software project

Background: On projects with tight schedules and limited budgets, it may not be possible to resolve all known bugs before the next release. Estimates of the time required to fix known bugs (the “bug fixing time”) would assist managers in allocating bug fixing resources when faced with a high volume of bug reports.

Aim: In this work, we aim to replicate a model for predicting bug fixing time with open source data from Bugzilla Firefox.

Method: To perform the replication study, we follow the replication guidelines put forth by Carver [J. C. Carver, Towards reporting guidelines for experimental replications: a proposal, in: 1st International Workshop on Replication in Empirical Software Engineering, 2010.]. Similar to the original study, we apply a Markov-based model to predict the number of bugs that can be fixed monthly. In addition, we employ Monte-Carlo simulation to predict the total fixing time for a given number of bugs. We then use the k-nearest neighbors algorithm to classify fixing times into slow and fast.

Result: The results of the replicated study on Firefox are consistent with those of the original study. The results show that there are similarities in the bug handling behaviour of both systems.

Conclusion: We conclude that the model that estimates the bug fixing time is robust enough to be generalized, and we can rely on this model for our future research.