Generating Test Data for Structural Testing Based on Ant Colony Optimization
Chengying Mao, Xinxin Yu, Jifu Chen, Jinfu Chen · 2012
Software testing has been always viewed as an effective way to ensure software quality both in academic and industry. In fact, the quality of test data set plays a critical role in the success of software testing activity. According to the basic line of search-based software testing, we introduced ant colony optimization (ACO) to settle this problem and proposed a framework of ACO-based test data generation. In our algorithm TDG_ACO, the local transfer rule, global transfer rule and pheromone update rule are re-defined to handle the continuous input domain searching. Meanwhile, the most widely-used coverage criterion, i.e., branch coverage, is adopted to construct fitness function. In order to validate the feasibility and effectiveness of our method, five real-world programs are utilized to perform experimental analysis. The results show that our algorithm outperforms the existing simulated annealing and genetic algorithm in most cases.