Flower Pollination Algorithm for Effective Test Case Optimization in Software Testing

Abstract

Software testing is measured as an significant way to guarantee software reliability and trustworthiness. Test case optimization shows a major role in software testing and quite a few methods are proposed to improve the fitness of the test case. The effect required to build the software is termed as cost. The fitness value is evaluated over the software that is later considered as cost value and an increase in the fitness value decrease the cost of the software. In this research, the Flower Pollination Algorithm (FPA) is proposed for the test case optimization. FPA is the recently developed algorithm and this is developed based on the function of the flower pollination. The local and global optimization is processed in the ATM machine software and a test case is optimized in the software. The ATM machine function is represented in State chart diagram graph and Sequence diagram graph. Then these two graphs are combined for the effective representation of the withdrawal process. The FPA algorithm has better convergence characteristics than the other Meta heuristic algorithm. The proposed FPA algorithm has a higher fitness value for the test case data is 52.06 %, while existing method has a higher fitness value for the test case data is 45 %.