Test Case Minimization using Genetic Algorithm: Pilot Study

Abstract

Due to the large number of test cases that are required to perform sufficient testing of the desired software; the diverse methods to reduce the test suite is needed. One of the common studied methods is removing the redundant test cases. In this research paper a short study is conducted to address the effectiveness of genetic algorithm in order to reduce the number of test cases that do not added tangible value in the mean of test coverage. Genetic algorithm is utilized in this study to help in minimizing the test cases, where the genetic algorithm generates the preliminary population, calculates the fitness value using coverage, and then selective the offspring in consecutive generations using genetic operations. This process of generation is repeated until a minimized test case is achieved. The results of study demonstrate that, genetic algorithms can significantly reduce the size of the test cases.