An orchestrated survey of methodologies for automated software test case generation

•We present an orchestrated survey of the most prominent techniques for automatic generation of software test cases.•The survey covers symbolic execution, model-based, combinatorial, search-based and adaptive random testing techniques.•For each technique, basic concepts and research challenges are reported, and future research directions are envisioned.•Each technique is surveyed by a world renowned expert(s) and active researcher(s) on the topic in a self-contained section.•The experts’ contributions were coordinated and the paper was edited by the orchestrators/editors into a consistent paper. Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different 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 world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of 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 a comprehensive and authoritative treatment.