Test case prioritization using modified genetic algorithm and ant colony optimization for regression testing

Regression testing (RT) plays an essential role in software maintenance. The occurrence of any new fault during the re-testing or modification process needs to be analyzed effectually. RT needs enormous effort to produce a higher fault detection rate. Test case prioritization (TCP) is an efficient way to predict the fault detection rate. Various researchers modelled the TCP method to provide a single objective solution; however, this work concentrates on providing a multi-objective solution using a meta-heuristic optimization approach. Here, two different approaches known as ant colony optimization (ACO) and genetic algorithm (GA) are adopted to offer a multi-objective solution with a better fault detection rate. The characteristics of ACO and GA are analyzed to prioritize the test case by combining the multi-dimensional characteristics under the test environment to enhance fault detection rate. Here, some experimentation is made to compute the performance of the proposed model by evaluating the number of test cases, number of iterations, and ant traversal path. The proposed model shows better trade-off in contrast to the prevailing approaches where the fault detection rate of multi-objective (ACO and GA) model provides outcomes of 94.5%, 94.8%, 93.8%, 92.5%, 95.8%, 97.8%, 99.2%, and 93.5% respectively.