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  • 1.
    Ferrari, Fabiano C.
    et al.
    Federal University of São Carlos, Brazil.
    Durelli, Vinicius H. S.
    Federal University of São João del-Rei, Brazil.
    Andler, Sten F.
    University of Skövde, Sweden.
    Offutt, Jeff
    University at Albany, USA.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Müllner, Nils
    DLR German Aerospace Center, Germany.
    On transforming model‐based tests into code: A systematic literature review2023In: Software testing, verification & reliability, ISSN 0960-0833, E-ISSN 1099-1689Article in journal (Refereed)
    Abstract [en]

    Model-based test design is increasingly being applied in practice and studied in research. Model-based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT: (i) some studies did not fully describe how tools transform abstract tests into concrete tests, (ii) some studies overlooked the computational cost of model-based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments.

  • 2.
    Saadatmand, Mehrdad
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Lindström, Birgitta
    University of Skövde, Sweden.
    Aichernig, Bernhard Klaus
    Graz University of Technology, Austria.
    Special issue on testing extra-functional properties2020In: Software testing, verification & reliability, ISSN 0960-0833, E-ISSN 1099-1689, Vol. 30, no 1, article id e1726Article in journal (Other academic)
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  • 3.
    Vercammen, Sten
    et al.
    University of Antwerp, Belgium.
    Demeyer, Serge
    University of Antwerp, Belgium; Flanders Make, Belgium.
    Borg, Markus
    RISE Research Institutes of Sweden, Digital Systems. Lund University, Sweden.
    Pettersson, Niklas
    Saab AB, Sweden.
    Hedin, Görel
    Saab AB, Sweden.
    Mutation testing optimisations using the Clang front-end2023In: Software testing, verification & reliability, ISSN 0960-0833, E-ISSN 1099-1689Article in journal (Refereed)
    Abstract [en]

    Mutation testing is the state-of-the-art technique for assessing the fault detection capacity of a test suite. Unfortunately, a full mutation analysis is often prohibitively expensive. The CppCheck project for instance, demands a build time of 5.8Â min and a test execution time of 17Â s on our desktop computer. An unoptimised mutation analysis, for 55,000 generated mutants took 11.8Â days in total, of which 4.3Â days is spent on (re)compiling the project. In this paper, we present a feasibility study, investigating how a number of optimisation strategies can be implemented based on the Clang front-end. These optimisation strategies allow to eliminate the compilation and execution overhead in order to support efficient mutation testing for the C language family. We provide a proof-of-concept tool that achieves a speedup of between 2 (Formula presented.) and 30 (Formula presented.). We make a detailed analysis of the speedup induced by the optimisations, elaborate on the lessons learned and point out avenues for further improvements.

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