Applying genetic algorithm to increase the efficiency of a data flow-based test data generation approach

Abstract

The success or failure of the entire software development process relies on the software testing component which is responsible for ensuring that the software that is released is free from bugs. One of the major labor intensive activities of software testing is the generation of the test data for the purpose of applying the testing methodologies. Many approaches have been tried and tested for automating the process of generating the test data. Meta-heuristics have been applied extensively for improving the efficiency of the process. This paper analyses the effectiveness of applying genetic algorithms for generating test data automatically using data flow testing approach. An incremental coverage measurement method is used to improve the convergence.