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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | A Bayesian Network Based Approach for Change Coupling Prediction |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 27 - 36 |
| Event Title | Working Conference on Reverse Engineering |
| Event Type | conference |
| Event Location | Antwerp, Belgium |
| Event Start Date | October 15 - 2008 |
| Event End Date | October 18 - 2008 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1109/WCRE.2008.39 |
| Other Identification Number | merlin-id:277 |
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