Using structural and textual information to capture feature coupling in object-oriented software
Empirical Software Engineering
An adaptive approach to impact analysis from change requests to source code
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Using Formal Concept Analysis to support change analysis
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Integrated impact analysis for managing software changes
Proceedings of the 34th International Conference on Software Engineering
Combining concept lattice with call graph for impact analysis
Advances in Engineering Software
A comparative study of static CIA techniques
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
Using water wave propagation phenomenon to study software change impact analysis
Advances in Engineering Software
WAVE-CIA: a novel CIA approach based on call graph mining
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Proceedings of the 10th Working Conference on Mining Software Repositories
What can commit metadata tell us about design degradation?
Proceedings of the 2013 International Workshop on Principles of Software Evolution
Studying software evolution using topic models
Science of Computer Programming
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The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Information Retrieval (IR) is used to derive conceptual couplings from the source code in a single version (release) of a software system. Evolutionary couplings are mined from source code commits. The premise is that such combined methods provide improvements to the accuracy of impact sets. A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, and KOffice is also reported. The results show that a combination of these two techniques, across several cut points, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in recall values of up to 20% over the conceptual technique in KOffice and up to 45% over the evolutionary technique in iBatis were reported.