USING COVERAGE INFORMATION TO PREDICT THE COST-EFFECTIVENESS OF REGRESSION TESTING STRATEGIES

Selective regression testing strategies attempt to choose an appropria te subset of test cases from among a previously run test suite for a s oftware system, based on information about the changes made to the sys tem to create new versions. Although there has been a significant amou nt of research in recent years on the design of such strategies, there has been very little investigation of their cost-effectiveness. This paper presents some computationally efficient predictors of the cost-e ffectiveness of the two main classes of selective regression testing a pproaches. These predictors are computed from data about the coverage relationship between the system under test and its test suite. The pap er then describes case studies in which these predictors were used to predict the cost-effectiveness of applying two different regression te sting strategies to two software systems; In one case study, the TESTT UBE method selected an average of 88.1 percent of the available test c ases In each version, while the predictor predicted that 87.3 percent of the test cases would be selected on average.