Improving Feature Location by Combining Dynamic Analysis and Static Inferences

Identifying the code needed to perform software maintenance and evolution tasks can be very difficult and time consuming for large, complex software systems. A number of techniques have been proposed that employ either static or dynamic analysis to isolate code related to a feature of interest. Static approaches suffer from poor precision by including irrelevant code, while dynamic approaches can suffer from poor recall by excluding relevant code. This paper presents and evaluates a hybrid approach for feature location that augments execution trace analysis with an analysis based on the program structure and associated semantics. This approach improves the completeness of feature location results by expanding the mapping of features to code provided by dynamic analysis in light of the static structure and semantics of the program. The approach is evaluated relative to a well-known dynamic approach using several features in an open source system.