SENSA: Sensitivity Analysis for Quantitative Change-Impact Prediction

Abstract

Sensitivity analysis determines how a system responds to stimuli variations, which can benefit important software-engineering tasks such as change-impact analysis. We present SENSA, a novel dynamic-analysis technique and tool that combines sensitivity analysis and execution differencing to estimate the dependencies among statements that occur in practice. In addition to identifying dependencies, SENSA quantifies them to estimate how much or how likely a statement depends on another. Quantifying dependencies helps developers prioritize and focus their inspection of code relationships. To assess the benefits of quantifying dependencies with SENSA, we applied it to various statements across Java subjects to find and prioritize the potential impacts of changing those statements. We found that SENSA predicts the actual impacts of changes to those statements more accurately than static and dynamic forward slicing. Our SENSA prototype tool is freely available for download.