An Effective Approach for Regression Test Case Selection Using Pareto Based Multi-Objective Harmony Search

Regression testing is a way of catching bugs in new builds and releases to avoid the product risks. Corrective, progressive, retest all and selective regression testing are strategies to perform regression testing. Retesting all existing test cases is one of the most reliable approaches but it is costly in terms of time and effort. This limitation opened a scope to optimize regression testing cost by selecting only a subset of test cases that can detect faults in optimal time and effort. This paper proposes Pareto based Multi-Objective Harmony Search approach for regression test case selection from an existing test suite to achieve some test adequacy criteria. Fault coverage, unique faults covered and algorithm execution time are utilised as performance measures to achieve optimization criteria. The performance evaluation of proposed approach is performed against Bat Search and Cuckoo Search optimization. The results of statistical tests indicate significant improvement over existing approaches.