Abstract
Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.
Highlights
Software testing is the implementation of the system and components and the behavior of observing and recording results under given conditions [1]
The Test cases (TC) generation mainly relied on manpower, which means that software testers need to have rich experience in software testing
In order to overcome the shortage of the ant colony optimization, in this paper, we put forward ILPACO, IPVACO, IGPACO, and a comprehensive improved ant colony optimization (ACIACO) and build ant colony research path model to improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations
Summary
Software testing is the implementation of the system and components and the behavior of observing and recording results under given conditions [1]. In 2003, McMinn and Holcombe [4] introduced heuristic ant colony optimization into the software test case generation technology for the first time. As the ant colony algorithm is being used widely gradually in the test case generation technology, many tools for software testing based on ACO have been developed. Suri and Singhal [10] proposed ACO TCSP tool which is based on ACO algorithm to select TC and greatly reduces the number of cases to 62.5%. In order to overcome the shortage of the ant colony optimization, in this paper, we put forward ILPACO, IPVACO, IGPACO, and ACIACO and build ant colony research path model to improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations
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