Using the SCADE Toolchain to Generate Requirements-Based Test Cases for an Adaptive Cruise Control System

Aniculăesei, Adina; Vorwald, Andreas; Rausch, Andreas

doi:10.1109/models-c.2019.00079

Cited by 6 publications

(13 citation statements)

References 8 publications

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“… Test cases are fully automatically transformed from model into code by using specific industrial or tailor‐made tools. Software specifications are automatically transformed into code, and test cases generated to cover the specification are automatically applied to code without manual intervention [16,17,20,24,31‐33,36,46]. Abstract, model‐level test cases are transformed into concrete, code‐level test cases after stepwise model‐to‐model and model‐to‐code transformations that are performed either automatically or by hand.…”

Section: Analysis Based On the Research Questionsmentioning

confidence: 99%

“…AutoMOTGen, by Mohalik et al [33], is an example of tool that has been developed to automatically generate tests from Simulink/Stateflow models using model checking. Aniculaesei et al [24] sought to explore model checking for the automatic generation of test cases based on requirements for test cruise control systems for the automotive industry. Essentially, the authors devised an approach in which system requirements are formalized into Linear Temporal Logic (LTL) language requirements, which is then used to generate test cases.…”

Section: Analysis Based On the Research Questionsmentioning

confidence: 99%

“…Aniculaesei et al [24] evaluated the coverage in terms of a mutation score. In their experiment, which used a single subject, the test set generated by the SCADE toolchain was able to kill roughly 21% of the mutants (a very low mutation score of 0.21).…”

Section: Analysis Based On the Research Questionsmentioning

confidence: 99%

“…In additional round (from experts), studies are listed in columns related to backward snowballing for convenience. For discussing RQ1, we grouped the 23 studies listed in the first line of Table 4 as follows: studies that directly provided information regarding the transformation of test cases across the abstraction levels by describing the tool that supports the transformation [16,17,20,24,[31][32][33]36,46], studies that described procedures for transforming test cases from models to code [25,34,[38][39][40][41]44,45,47] and studies that just reported that test cases developed at the model level are then applied to test the code [21][22][23]26,43]. Studies from the three groups are discussed in the sequence.…”

Section: Analysis Based On the Research Questionsmentioning

confidence: 99%

“…Aniculaesei et al [24] compared the fault revealing capability of test sets automatically generated with a commercial tool (the SCADE tool suite and an academic, open-source tool (NuSMV) that applies the model checking approach. Both tools turn models and test cases generated at the model level into C code, and the study assessed the effectiveness of the test sets based on their mutation scores.…”

Section: Rq # Of Studiesmentioning

confidence: 99%

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On transforming model‐based tests into code: A systematic literature review

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Durelli,

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et al. 2023

Software Testing Verif & Rel

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SummaryModel‐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.

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