iFL: An interactive environment for understanding feature implementations

Abstract

We propose iFL, an interactive environment that is useful for effectively understanding feature implementation by application of feature location (FL). With iFL, the inputs for FL are improved incrementally by interactions between users and the FL system. By understanding a code fragment obtained using FL, users can find more appropriate queries from the identifiers in the fragment. Furthermore, the relevance feedback obtained by partially judging whether or not a fragment is relevant improves the evaluation score of FL. Users can then obtain more accurate results. Case studies with iFL show that our interactive approach is feasible and that it can reduce the understanding cost more effectively than the non-interactive approach.