Optimization of software testing using genetic algorithms

This paper presents the study of optimization of software testing techniques by using Genetic Algorithms (GAs) and a sufficient testing convergence condition of GAs is presented. Some new categories of genetic codes are applied in some problem optimizations for the generation of reliable software test cases. These GAs have found their application in detecting errors in the software packages. For example, based on Symmetric Codes theory, new genetic strategy, GA with symmetric code is developed. In the current paper, some key definitions of genetic transformation have been used viz. crossover, mutation and selection. Some of our research shows that genetic encoding techniques have very important influence on the performance of software test cases. This paper is organized into three parts: part I describes the functionality of GAs, part II presents the usage of GAs in software testing to the alternatives of existing software testing techniques, part III discusses the implementation of GAs using MATLAB for the generation of optimized test cases.