Automated Software Test Data Generation for Data Flow Dependencies using Genetic Algorithm

Software testing is one of the most labor-intensive and expensive phase of the software development life cycle. Software testing includes test case generation and test suite optimization that has a strong impact on the effectiveness and efficiency of software testing. Over the past few decades, there has been active research to automate the process of test case generation but the attempts have been constrained by the size and the complexity of software. The use of metaheuristic global search techniques for software test data generation has been the focus of researchers in recent years. Many new techniques and hybrid methods have also been proposed to tackle the problem more effectively. This study proposes a novel approach based on genetic algorithm to generate test data for a program. The performance of the proposed approach is evaluated based on data flow dependencies of a program by comparing it with random testing. Based on the experimental results on a number of C programs, it is shown that the proposed approach outperforms random testing in test data generation and optimization. Keywords—Search Based Software Testing, Automated Test Data Generation, Metaheuristic Search Algorithms, Evolutionary Algorithms, Genetic Algorithms, Data Flow Dependencies