An Orchestrated Survey on Automated Software Test Case Generation I

Abstract

Test case generation is among the most labour-intensive tasks in software testing and also one that has a strong impact on the e ectiveness and e ciency of software testing. For these reasons, it has also been one of the most active topics in the research on software testing for several decades, resulting in many di erent approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by worldrenowned active researchers on the technique, and briefly covers the basic ideas underlying the technique, the current state of art, a discussion of the open research problems, and a perspective of the future development in the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring comprehensiveness and authoritativeness.