Automated generation of software testing path based on ant colony
Faeghe Sayyari, Sima Emadi · 2015
Software testing is one of the expensive and time consuming processes and many studies have been conducted to facilitate and perform it automatically. One of the most important topics in software testing is developing the test path to generated test data and coverage of the generated path. Optimization methods can be used to solve the problem of path testing. Heuristic search methods especially evolutionary algorithms are cost savings and can be effective in the automated generation of test paths. One of the most important challenges in the development of path tests is lack of full coverage of defined nodes and ignoring the important parameters of user. In this study, a solution is proposed based on ant colony optimization algorithm and model-based testing to develop test paths faster with maximum coverage and minimum time and cost. The model in this study is based on Markov chain. The results obtained from Markov chain are good choices for studying the viability of the testing process while developing them. Evaluation of the proposed algorithm has shown better performance compared to existing methods in terms of cost, coverage, time and parameters of user.