Empirical Software Change Impact Analysis using Singular Value Decomposition

Verification and validation techniques often generate various forms of software development artifacts. Change records created from verification and validation efforts show how files in the system tend to change together in response to fixes for identified faults and failures. We propose a methodology for determining the impact of a new system modification by analyzing software change records through singular value decomposition. This methodology generates clusters of files that historically tend to change together to address faults and failures found in the code base. We performed a post hoc case study using this technique on five open source software systems. We determined that our technique was effective in identifying impacted files in a system from an introduced change when the developers tended to make small, targeted updates to the source system regularly. We further compared our technique against two other impact analysis techniques (Pathlmpact and Coveragelmpact) and found that our technique provided comparable results, while also identifying non-source files that could be impacted by the change.