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An orchestrated survey of methodologies for automated software test case generation

By Saswat Anand, Edmund K. Burke, Tsong Yueh Chen, John Clark, Myra B. Cohen, Wolfgang Grieskamp, Mark Harman, Mary Jean Harrold, Phil McMinn, Antonia Bertolino, J. Jenny Li and Hong Zhu

Abstract

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

Topics: Adaptive random testing, Combinatorial testing, Model-based testing, Orchestrated survey, Search-based software testing, Software testing, Symbolic execution, Test automation, Test case generation
Publisher: Elsevier
Year: 2013
DOI identifier: 10.1016/j.jss.2013.02.061
OAI identifier: oai:vtl.cc.swin.edu.au:swin:33653
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