Blending Conceptual and Evolutionary Couplings to Support Change Impact Analysis in Source Code

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.