Abstract

System evolution depends greatly on the ability of a maintainer to locate these parts of the source code that implement specific features. Until recently, quite a number of feature location techniques have been proposed. These techniques suffer from a number of limitations. They either require exercising several features of the system, or rely heavily on domain experts to guide the feature location process. In this thesis, we present a novel approach for feature location that combines static and dynamic analysis techniques. An execution trace is generated by exercising the feature under study (dynamic analysis). A component dependency graph (static analysis) is used to rank the components invoked in the trace according to their relevance to the feature. Our ranking technique is based on the impact of a component modification on the rest of the system. We hypothesize that the smaller the impart of a component modification, the more likely it is that this component is specific to the feature. The proposed approach is automatic to a large extent relieving the user from any decision that would otherwise require extensive knowledge of the system. We present a case study involving features from two software systems to evaluate the applicability and effectiveness of our approach.