| At present,with the progress of science and technology,there is a qualitative improve the performance of hardware products,especially the storage device and the computing power makes enormous progress,the scale of software products become very complex,how to guarantee the quality of software products has become a problem can't be ignored.In the whole life cycle of software development,software testing is the key link to ensure the quality of software,and its role is becoming more and more important.In software testing process,one of the difficulties of testing is to choose test cases,which is directly related to whether the errors can meet the expected design goals,and is detected in the testing process.One of the most important technologies in software testing technology is the generation of test cases.The current manual construction test case is limited,not only the heavy workload,the long construction cycle,but also easy to omit the full coverage.Therefore,it is of great practical significance for the quality of software development and the reduction of the development cost to realize the efficient and rapid test case generation.For some of the existing methods of automatically generating software test cases,although it has some practical applications,but because of the low efficiency of generating test cases,it has great limitations for the rapid development of software technology.At present,there is still no way to solve this problem perfectly,it can only be judged by practical experience in specific engineering,which seriously limits the development of testing technology.Therefore,the study of how to solve these problems is of great significance both in engineering and in the academic field.In this case,is presented in this paper to study the method of test case generation improved ant colony algorithm,is designed for improvement based on the existing work on the basis of other scholars,in order to improve the limitations of existing methods,the test cases generated more efficiently,the main work is as follows:First,the ant colony algorithm is used to solve the better solution process.After repeated evolution,it is easy to appear premature and stagnation,and the global search ability is reduced,resulting in the best solution is the local optimal solution.In this paper,an ant colony adaptive ant colony algorithm based on parallel multigroup is proposed.The algorithm divides the ant population into multiple groups at the same time,several groups of parallel search,improve the search path diversity,which makes the algorithm not premature convergence;and the introduction of the diversity of the population,the migration rules of ants can adaptively adjust,and updating pheromone concentration with fitness function as weight adaptive.The experimental results show that the search ability of this method has some advantages compared with the basic ant colony algorithm,and the convergent algebra is much less.Therefore,the improvement of the algorithm is effective.Second,this paper finds that the test case generated by parallel multi population adaptive ant colony algorithm is large,which is not conducive to improving the efficiency and cost saving of test case execution.So this paper combines quantum optimization with multi swarm adaptive ant colony algorithm,and proposes quantum multi population adaptive ant colony algorithm,in order to optimize the scale and efficiency of test case generation.In this algorithm the quantum bit to represent the ants in the colony,to maintain the diversity of population,the quantum state vector algorithm,ant colony individuals are updated by quantum rotation gates and quantum gates.In this paper,we compare the algorithm with other 3 swarm intelligence algorithms,by comparing the average convergence algebra,average running time and other performance indicators of several algorithms.The experimental results show that the quantum multi-population adaptive ant colony algorithm proposed in this paper is accurate and effective. |
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