Test Case Optimized Generation Method Based On Improved PSO Algorithm

The combinatorial test technique is an effective testing method that finds faults by detecting all combinations of values in software system parameters.Internal and external interactions in software may lead to failures.With the continuous development of technology and the increase of the software complexity,the combination space of the factors becomes extremely large,making it hard to comprehensively cover the combinatorial testing cases.Hence,choosing an efficient way to select and generate testing cases with small scale and high error detection rate is a key task in the field of combinatorial testing.In the field of software testing,the meta heuristic search algorithm has solved the NP-complete problem of combinatorial testing,and the particle swarm optimization possesses its unique competitive advantage.This paper systematically reviews and summarizes the research results of PSO(PSO,Particle Swarm Optimization)algorithm in the generation of the set of combinatorial testing cases.Aiming at the reduction problem for invalid combination,the parameter problem of particle swarm optimization algorithm and the local optimal problem caused by premature convergence of algorithm,this paper combines the improved IPO(in-parameter-order)strategy and the adaptive simplified particle swarm optimization algorithm,and proposes a new method for generating combinatorial testing cases,which can satisfy arbitrary strength covering arrays and possess advantages in time and space.The main contributions of this work are summarized as follows:(1)Aiming at the problem of constraint relationship between actual input factors,a t-way combinatorial generation method with constraint processing is proposed.When combining these factors,constraint processing based on equivalence partition and restrictions is used to avoid generating excessive invalid testing cases and narrow the range of testing cases,thereby improving the validity of the test and increasing its accuracy to some extent.(2)Aiming at the factor sorting selection problem of IPO strategy,an improved method is proposed.The core idea of the method is to use the error detection rate Ci and response time Ti of testing cases as the evaluation index,and weight the evaluation index to obtain non,incremental ordering of each factor.After sorting,the factor selection can effectively avoid the random problem of the original strategy.(3)Aiming at the parameter setting problem of PSO algorithm,the daptive simplified particle swarm optimization algorithm makes more reasonable settings for the two parameters of velocity variable and inertia weight.The algorithm eliminates the influence of the unnecessary factor of speed and dynamically updates the inertia weight according to the motion state of the particle itself,which not only speeds up the operation speed but also enhances the search ability of the particle group later.This technique can effectively avoid the particle falling into local optimum and enhance the algorithm performance.In order to verify the effectiveness of the improved PSO algorithm in the generation of combinatorial testing cases,the improved algorithm proposed in this paper is implemented by Java language programming and compared with the original algorithm.The experimental results show that the improved algorithm proposed in this paper has certain advantages in terms of the size of testing cases and execution time under certain conditions.