Localizing failure-inducing program edits based on spectrum information

Keeping evolving systems fault free is hard. Change impact analysis is a well-studied methodology for finding faults in evolving systems. For example, in order to help developers identify failure-inducing edits, Chianti extracts program edits as atomic changes between different program versions, selects affected tests, and determines a subset of those changes that might induce test failures. However, identifying real regression faults is challenging for developers since the number of affecting changes related to each test failure may still be too large for manual inspection. This paper presents a novel approach FAULTTRACER which ranks program edits in order to reduce developers' effort in manually inspecting all affecting changes. FAULTTRACER adapts spectrum-based fault localization techniques and applies them in tandem with an enhanced change impact analysis that uses Extended Call Graphs to identify failure-inducing edits more precisely. We evaluate FAULTTRACER using 23 versions of 4 real-world Java programs from the Software Infrastructure Repository. The experimental results show that FAULTTRACER outperforms Chianti in selecting affected tests (slightly better, but handles safety problems of Chianti) as well as in determining affecting changes (with an improvement of approximately 20%). By ranking the affecting changes using spectrum-based test behavior profile, for 14 out of 22 studied failures, FAULTTRACER places a real regression fault within top 3 atomic changes, significantly reducing developers' effort in inspecting potential failure-inducing edits.