A Bayesian Network Based Approach for Change Coupling Predi...

A Bayesian Network Based Approach for Change Coupling Prediction

inproceedings
Emanuel Giger, Harald C. Gall, Yu Zhou, Michael Würsch, Jian Lue
Source code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available. Based on our previous research, in this paper, we inspect different dimensions of software changes including change significance or source code dependency levels, extract a set of features from the two sources and propose a bayesian network-based approach for change coupling prediction. By combining the features from the co-changed entities and their dependency relation, the approach can model the underlying uncertainty. The empirical case study on two medium-sized open source projects demonstrates the feasibility and effectiveness of our approach compared to previous work.
A Bayesian Network Based Approach for Change Coupling Prediction
2008
IEEE Computer Society
WCRE '08: Proceedings of the 2008 15th Working Conference on Reverse Engineering
978-0-7695-3429-9
27--36
http://dx.doi.org/10.1109/WCRE.2008.39
Antwerp, Belgium
s.e.a.l.