Empirical Studies of a Prediction Model for Regression Test Selection

ÐRegression testing is an important activity that can account for a large proportion of the cost of software maintenance. One approach to reducing the cost of regression testing is to employ a selective regression testing technique that 1) chooses a subset of a test suite that was used to test the software before the modifications, then 2) uses this subset to test the modified software. Selective regression testing techniques reduce the cost of regression testing if the cost of selecting the subset from the test suite together with the cost of running the selected subset of test cases is less than the cost of rerunning the entire test suite. Rosenblum and Weyuker recently proposed coverage-based predictors for use in predicting the effectiveness of regression test selection strategies. Using the regression testing cost model of Leung and White, Rosenblum and Weyuker demonstrated the applicability of these predictors by performing a case study involving 31 versions of the KornShell. To further investigate the applicability of the Rosenblum-Weyuker (RW) predictor, additional empirical studies have been performed. The RW predictor was applied to a number of subjects, using two different selective regression testing tools, DejaVu and TestTube. These studies support two conclusions. First, they show that there is some variability in the success with which the predictors work and second, they suggest that these results can be improved by incorporating information about the distribution of modifications. It is shown how the RW prediction model can be improved to provide