Abstract
Software has become an important part of our daily lives. Due to user requests, new features are developed. To ensure the quality of a software, new test cases have to be generated and included in the current test pool. Further, the size of the test pool will often grow too large to test through all of the test cases. A very large test pool will take large amounts of time to produce inefficient regression testing. Test suite reduction is typically used to solve the test suite size problem by removing redundant test cases. By using test suite reduction, the test pool size can be efficiently reduced, while the remaining test cases are still able to provide the same coverage as the original test pool. However, some of the existing test suite reduction methods consider only one testing criterion. If we use more than one testing criterion, we can improve the capability of fault detection effectiveness. Also, some people consider two testing criteria; however, they don’t use the second criterion until there is a tie situation in the primary criterion. The main purpose of this research is to use 4 modified weighted combinatorial algorithms to improve the effectiveness of fault detection, and to consider two different types of testing criteria, flexibly and simultaneously. Further, we also discuss how to incorporate a genetic algorithm to find the best weighting factor value assignments for each testing criterion. Experimental results show that 77.5% of our proposed weighted combinatorial algorithms have an even or better performance in suite size reduction, 83.75% of our approaches perform even or better in fault detection effectiveness, and 82.5% of our approaches have even or better effectiveness in fault-to-test ratio.