Skip to Main Content
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.