Authors : Jyoti Chaudhary and Mukesh Kumar
Abstract: Software development associations spend extensive part of their financial plan and time in testing related exercises. The adequacy of the verification and validation process relies on the number of errors found and corrected before releasing the software to the customer side by means of testing procedure. Mutation testing is a fault-based programming testing procedure that has been broadly concentrated on for more than three decades. As of late, evolutionary or optimization algorithms have been demonstrated reasonable for decreasing the cost of test case generation in the context of mutation testing. In this study, we develop a new combination of optimization algorithms called a hybrid Artificial Bee Colony-Penguin Search Optimization (ABC-PeSO) approach for generating efficient test input data in the context of mutation testing to decrease the cost of such a test scheme. Here, in this hybrid method, the ABC algorithm is enhanced by converting the random search process of scout bee phase to randomized process by using PeSO. Also, to evaluate the performance of the proposed ABC-PeSO, a detail comparison is conducted for different algorithms like ABC, PeSO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The comparison between the proposed and existing method in behalf of two benchmark programs as mutation score and path coverage. According to the analysis, it is concluded that proposed ABC-PeSO approach has produced better results.
Jyoti Chaudhary and Mukesh Kumar, 2017. Optimal Test Case Generation in Mutation Testing-A Hybrid Artificial Bee Colony-Penguin Search Optimization (ABC-PeSO) Approach. Journal of Engineering and Applied Sciences, 12: 6635-6648.