Hybrid Program Dependence Approximation for Effective Dynamic Impact Prediction

Abstract

Impact analysis determines the effects that program entities of interest, or changes to them, may have on the rest of the program for software measurement, maintenance, and evolution tasks. Dynamic impact analysis could be one major approach to impact analysis that computes smaller impact setsthan static alternatives for concrete sets of executions. However, existing dynamic approaches often produce impact sets that are too large to be useful, hindering their adoption in practice. To address this problem, we propose to exploit static program dependencies to drastically prune false-positive impacts that are not exercised by the set of executions utilized by the analysis, via hybrid dependence approximation. Further, we present a novel dynamic impact analysis called Diver which leverages both the information provided by the dependence graph and method-execution events to identify runtimemethod-level dependencies, hence dynamic impact sets, much more precisely without reducing safety and at acceptable costs. We evaluate Diver on ten Java subjects of various sizes and application domains against both arbitrary queries covering entire programs and practical queries based on changes actually committed by developers to actively evolving software repositories. Our extensive empirical studies show that Diver can significantly improve the precision of impact prediction, with 100-186 percent increase, with respect to a representative existing alternative thus provide a far more effective option for dynamic impact prediction. Following a similar rationale to Diver, we further developed and evaluated an online dynamic impact analysis called DiverOnline which produces impact sets immediately upon the termination of program execution. Our results show that compared to the offline approach, for the same precision, the online approach can reduce the time by 50 percent on average for answering all possible queries in the given program at once albeit at the price of possibly significant increase in runtime overhead. For users interested in one specific query only, the online approach may compute the impact set for that query during runtime without much slowing down normal program operation. Further, the online analysis, which does not incur any space cost beyond the static-analysis phase, may be favored against the offline approach when trace storage and/or related file-system resource consumption becomes a serious challenge or even stopper for adopting dynamic impact prediction. Therefore, the online and offline analysis together offer complementary options to practitioners accommodating varied application/task scenarios and diverse budget constraints.