Research On Test Case Generation Based On Cuckoo Search

Software testing plays a vital role in the whole software development cycle,and it is an important part to ensure the quality of software.The technology of test case generation is the key link of automated software testing,which can reduce the resources of human,money and time in the process of software development,and improve the efficiency of software testing.Search-based test case generation transforms the problem of test case generation into a problem of function optimization by fitness function.Then the heuristic search algorithm solves the problem of function optimization,so it is vital to select the heuristic algorithm.As one of many heuristic algorithms,the cuckoo search has been widely applied to the field of test case generation due to its advantages such as clear theory,few parameters,easy expansion,and strong global search capability.The subject of this thesis is test case generation technology based on cuckoo search.Focused on this subject,our work is as follows:(1)In the process of test case generation in the cuckoo search,the chaotic mapping mechanism is introduced for the problem that random initialization may cause uneven distribution of bird nest locations.Cubic chaotic maps are used to initialize the population in order to increase the initial diversity of the nest and improve the efficiency of the algorithm.(2)In the process of test case generation in the cuckoo search,Teaching-learning mechanism is introduced.That is,the solution below the average fitness value in the evolution process is updated through Lévy flight,while the other part of the better solution utilizes the optimization of teaching-learning for local search.The cuckoo search can be used to exert its global search performance,and the teaching-learning optimization algorithm exerts its local optimization ability,which makes the global search and local search ability of the algorithm coordinated and improves the test case generation efficiency.(3)When implementing test cases,testers often want to measure the efficiency and effectiveness of the test from multiple perspectives.To solve this problem,multi-objective optimization is introduced into the test case generation process.First,in the multi-objective cuckoo search,the external archives are maintained in conjunction with the idea of crowd distance.Secondly,based on the two aspects of error discovery ability and test cost,the weighted branch distance and the test case size are selected as two optimization goals.Finally,a greedy algorithm is used to reduce redundant test cases,thereby reducing the number of test cases as much as possible.