LAUSR

Test case prioritization (TCP) is a widely accepted and extensively used strategy during regression testing. TCP is the permutation of test cases to enhance efficiency in achieving performance goals. These goals can belong to the category of single objective problem or multi-objective problem. This empirical study focuses on three objectives wherein two objectives are to be maximized and the remaining one minimized. During this study, three websites and various versions were created on which non-dominated sorting genetic algorithm-II and variant of non-dominated sorting artificial bee colony algorithm were applied to prioritize sequence of test cases. The problem size varies from small-size fault matrix $$(34\times 27)$$(34×27) to mid-size fault matrix $$(157\times 128)$$(157×128). Performance of the two algorithms was measured on various parameters and also verified on the basis of statistical testing. An alternate approach for solving this multi-objective problem, based on dynamic programming, is also proposed in this study, and it is concluded that performance of this algorithm is at par with other suggested ones.