Generalizing evolutionary coupling with stochastic dependencies
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Biometric feature extraction with biometric specific shape descriptors
International Journal of Biometrics
Controversy Corner: Preserving knowledge in software projects
Journal of Systems and Software
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During software evolution, developers modify various modules to handle new requirements or to fix existing bugs. Such changes usually propagate to related modules throughout the system. Program comprehension techniques are able to predict this change propagation phenomenon. In this paper, we introduce a novel approach that predicts the possible affected system modules, given a change in the system. We use Bayesian Belief Networks as a probabilistic tool to make such predictions in a systematic way. This novel technique mainly relies on two sources of information: dependency metrics (calculated using static analysis) and change history extracted from a version control repository. We evaluate our approach by examining all significant revisions of Azureus2, an open-source Java system. The results show that the predicted change probabilities reflect actual module changes even in the early stages of the software development.