Research On An Improved Genetic Algorithm Used In Test Cases For Automatic Generation

In software test process,efficient test case generation can dramatically simplify testing,improve test efficiency and save software development costs.Software test is the important means that guarantee software quality and reliability,and in this respect,it plays the role that other method cannot replace.However,software test is a complex process:it needs to consumer huge manpower,material resources and time,Therefore,raising the automation level of software test tool is very important to for ensure software development quality and reduction software development cost.And then,the most important is raising the automation level of the test case generation for raising the automation level of test tool and even entire test process,so this paper study and design mainly according to this problem,which seems very theoretical and practical significance.This article has first introduced the basic theory of software test and automatic generation technology of test case.In which,it has been elaborated emphatically that automatic generation technology of path wise test case and some existing realization methods.And then we pointed out the technology of artificial intelligence will successful in this field,according to the actual condition of this problem.Soon afterwards,we introduced realization step and the basic principle of genetic algorithm,and then analyzed the good and shortcoming.As an effective search algorithm,genetic algorithm has been widely applied to the study of automatic generation of test cases,and has good global search capability.However,some inherent limitations of this algorithm exist,such as low optimization efficiency,premature convergence,etc.This paper proposes two modified genetic algorithm for test case generation.The first modified genetic algorithm combined with tabu search algorithm,improves the select and crossover operator of genetic algorithm against the shortcomings of premature convergence,and adopt the optimal preservation strategy for improving search capabilities in the local space and the overall operating efficiency.Another combined with Artificial fish swarm algorithm(AFSA),which is an efficient intelligent optimization technique.The disadvantages of AFSA is researched,then a hybrid artificial fish swarm optimization algorithm based on genetic algorithm is presented.The hybrid algorithm break away from artificial fish moving without a definite purpose or heavy geeing together round the local optimum.It is simple and implement as AFSA,but can greatly improve the ability of efficiency and accuracy of seeking the global excellent result.The feasibility and effectiveness of the approach is verified through testing by some functions and practical problems.Experiments result shows that the new algorithm has obvious advantages in efficiency and effectiveness compared with traditional genetic algorithm for test case generation.