A Proportional Study Of Test Case Prioritization Using Hybrid Optimization Algorithm ,Cuckoo Search And Particle Swarm Optimization Techniques
Software Engineering deals with the event of software systems and to scale back the price and improve the event process. Software metric could even be a quantitative measure of a degree to which a software or process some property. it's consider because the foremost basis because it depends on the software quality. a replacement Hybrid Optimization Algorithm, a Hybridization Of Cuckoo Search And Particle Swarm Optimization (CSPSO), is proposed and compared during this paper for the optimization of continuous functions and engineering design problems. This algorithm are often considered some modifications of the recently developed Cuckoo Search (CS). These modifications involve the event of initial population, the dynamic adjustment of the parameter of the cuckoo search, and thus the incorporation of the particle swarm optimization (PSO). so on develop better quality software, we've considered as software metrics like reusability, fault proneness and alter proneness. In our proposed method test cases are optimized by using Particle Swarm Optimization algorithm (PSO). Finally, the optimized result are becoming to be evaluated by software quality measures. It typically uses genetic algorithms (GAs) to look for relevant test cases. it had been first introduced in 1995 by Kennedy and Eberhart. Inspired by social metaphors of behavior and swarm theory, simple methods were developed for efficiently optimizing non-linear mathematical functions.In this article, a comparative analysis of Genetic Algorithm (GA), Hybrid Optimization Algorithm, a Hybridization Of Cuckoo Search And Particle Swarm Optimization (CSPSO) in terms of path coverage, iterations and execution time and also explains about the varied optimization techniques on the thought of their evolution, methodology,performanceandapplications.