Using Genetic Algorithms in Test Data Generation

Abstract

Software testing activities account for a considerable portion of systems development cost and, for this reason, many studies have sought to automate these activities. Test data generation has a high cost reduction potential (especially for complex domain systems), since it can decrease human effort. Although several studies have been published about this subject, articles of reviews covering this topic usually focus only on specific domains. This article presents a systematic mapping aiming at providing a broad, albeit critical, overview of the literature in the topic of test data generation using genetic algorithms. The selected studies were categorized by software testing technique (structural, functional, or mutation testing) for which test data were generated and according to the most significantly adapted genetic algorithms aspects. The most used evaluation metrics and software testing techniques were identified. The results showed that genetic algorithms have been successfully applied to simple test data generation, but are rarely used to generate complex test data such as images, videos, sounds, and 3D (three-dimensional) models. From these results, we discuss some challenges and opportunities for research in this area.