Improving failure-inducing changes identification using coverage analysis

Delta debugging has been proposed for failure-inducing changes identification. Despite promising results, there are two practical factors that thwart the application of delta debugging: large number of tests and misleading false positives. To address the issues, we present a combination of coverage analysis and delta debugging that automatically isolates failure-inducing changes. Evaluations on twelve real regressions in GNU software demonstrate both the speed gain and effectiveness improvements.