The experimental applications of search-based techniques for model-based testing

Graphical abstractDisplay Omitted HighlightsA systematic review of applications of search-based techniques for model-based testing is provided.Four taxonomies are proposed to classify the applications based on the purpose, problems, solutions and evaluations.The applications are analyzed based on the proposed taxonomies.The development of search-based techniques for model-based testing is discussed.Limitations and potential research directions are summarized. ContextModel-based testing (MBT) aims to generate executable test cases from behavioral models of software systems. MBT gains interest in industry and academia due to its provision of systematic, automated, and comprehensive testing. Researchers have successfully applied search-based techniques (SBTs) by automating the search for an optimal set of test cases at reasonable cost compared to other more expensive techniques. Thus, there is a recent surge toward the applications of SBTs for MBT because the generated test cases are optimal and have low computational cost. However, successful, future SBTs for MBT applications demand deep insight into its existing experimental applications that underlines stringent issues and challenges, which is lacking in the literature. ObjectiveThe objective of this study is to comprehensively analyze the current state-of-the-art of the experimental applications of SBTs for MBT and present the limitations of the current literature to direct future research. MethodWe conducted a systematic literature review (SLR) using 72 experimental papers from six data sources. We proposed a taxonomy based on the literature to categorize the characteristics of the current applications. ResultsThe results indicate that the majority of the existing applications of SBTs for MBT focus on functional and structural coverage purposes, as opposed to stress testing, regression testing and graphical user interface (GUI) testing. We found research gaps in the existing applications in five areas: applying multi-objective SBTs, proposing hybrid techniques, handling complex constraints, addressing data and requirement-based adequacy criteria, and adapting landscape visualization. Only twelve studies proposed and empirically evaluated the SBTs for complex systems in MBT. ConclusionThis extensive systematic analysis of the existing literature based on the proposed taxonomy enables to assist researchers in exploring the existing research efforts and reveal the limitations that need additional investigation.