Efficient and precise dynamic impact analysis using execute-after sequences

As software evolves, impact analysis estimates the potential effects of changes, before or after they are made, by identifying which parts of the software may be affected by such changes. Traditional impact-analysis techniques are based on static analysis and, due to their conservative assumptions, tend to identify most of the software as affected by the changes. Researchers have begun to investigate dynamic impact-analysis techniques, which rely on dynamic, rather than static, information about software behavior. Existing dynamic impact-analysis techniques are either very expensive in terms of execution overhead or amount of dynamic information collected - or imprecise. In this paper, we present a new technique for dynamic impact analysis that is almost as efficient as the most efficient existing technique and is as precise as the most precise existing technique. The technique is based on a novel algorithm that collects (and analyzes) only the essential dynamic information required for the analysis. We discuss our technique, prove its correctness, and present a set of empirical studies in which we compare our new technique with two existing techniques, in terms of performance and precision.