Particle Swarm Optimization with Cross-Over Operator for Prioritization in Regression Testing
نویسندگان
چکیده
Software Testing is continuous process of development and maintenance in life of software. In maintenance phase, regression testing gets exercisedwith additional resources/time for performance. The prioritization of test cases helps to reduce the cost-time of regression testing. Hence, completing Regression Testing effectively and on schedule is challenge for software tester. In this research paper, the Particle Swarm Optimization (PSO) technology has been studied and used with the blend of Genetic Algorithm (GA) and the hybrid prioritized algorithm has been proposed. The Particle Swarm Optimization is an optimization algorithm based on heuristic search which can be used to solve time-constraint environment of Test Case Prioritization and the concept of Genetic Algorithm will further help in diversifying the solution within whole search space. For finding the effectiveness of hybrid prioritization algorithm: the efficiency %, saving %, reduction % and APFD/APCC has been calculated. KeywordsRegression Testing, Particle Swarm Optimization, Genetic Algorithms
منابع مشابه
Hybrid Particle Swarm Optimization for Regression Testing
Regression Testing ensures that any enhancement made to software will not affect specified functionality of software. The execution of all test cases can be long and complex to run; this makes it a costlier process. The prioritization of test cases can help in reduction in cost of regression testing, as it is inefficient to rerun each and every test case. In this research paper, the criterion c...
متن کاملA Hybrid Model of Particle Swarm and Ant Colony Optimization Algorithm for Test Case Optimization
Regression testing is the process of validating modifications introduced in a system during software maintenance. It is done to check that a system update does not introduce errors that have been corrected or the change in one part of the program does not affect the other modules of that program. As the test suite is very large, system retesting consumes large amount of time and computing resou...
متن کاملAn improved particle swarm optimization with a new swap operator for team formation problem
Formation of effective teams of experts has played a crucial role in successful projects especially in social networks. In this paper, a new particle swarm optimization (PSO) algorithm is proposed for solving a team formation optimization problem by minimizing the communication cost among experts. The proposed algorithm is called by improved particle optimization with new swap operator (IPSONSO...
متن کاملAn efficient specific update search domain based glowworm swarm optimization for test case prioritization
Software testing is an important activity that is carried out during the software development life cycle. Regression testing means re-executing test cases from existing test suites to assure that the modifications done to the existing software have no adverse effects. During regression testing, new test cases are not created but previously created test cases are reexecuted. The ideal regression...
متن کاملMulti-Objective Optimization of Solar Thermal Energy Storage Using Hybrid of Particle Swarm Optimization and Multiple Crossover and Mutation Operator
Increasing of net energy storage (Q net) and discharge time of phase change material (t PCM), simultaneously, are important purpose in the design of solar systems. In the present paper, Multi-Objective (MO) based on hybrid of Particle Swarm Optimization (PSO) and multiple crossover and mutation operator is used for Pareto based optimization of solar systems. The conflicting objectives are Q net...
متن کامل