Testing of Automotive Systems - Complex vs. Simple Environment Models

Sobotka, Jan; Krejei, Luka

doi:10.1109/bec.2018.8600953

Cited by 2 publications

(7 citation statements)

References 7 publications

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“…During the evaluation of this case study, several questions regarding the modeling techniques has been raised. Questions regarding the environment part of the system models were covered by previous experiments presented in [6]. However, during the experiments, new questions concerning observer part of the model were raised.…”

Section: Fig 1 Diagram Of the Mbt Process Using The Taster Toolmentioning

confidence: 99%

“…Consequently, that shows importance of chosen criteria. This paper works with the progress of coverage over time, just like previous experiments presented in [6]. This progress is influenced by exploration algorithm and by a model structure.…”

Section: Related Workmentioning

confidence: 99%

“…The trunk control system model is similar to the common observer model used in previous experiments presented in [6] and is shown on Fig. 3.…”

Section: Fig 2 the Locking System Observer Modelmentioning

confidence: 99%

“…Both the system environment and the SUT itself can be modeled in many ways -from fully permissive models similar to the random generation of test stimuli to entirely restrictive version allowing only specific test cases based on behavioral modeling [6]. Experiments presented in paper [6] have shown that both simple and complex modeling approaches for modeling of SUT environment are comparable in terms of achieved state space coverage. However, the experiments were focused only on coverage criteria and didn't cover the modelling of the observer part.…”

Section: Introductionmentioning

confidence: 99%

“…Experiments presented in this paper are based on the same case study used in previous experiments presented in [6], focused on the application of MBT applied on the Hardware-in-the-Loop (HiL) integration testing of the control unit of car trunk doors. The first part of the case study is the SUT model based on the system specification extended by the model of the control unit of car keyless locking system for purpose of experiments presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Relation Between Test Coverage and Timed Automata Model Structure

Krejci

Sobotka

Novák

2021

Communications in Computer and Information Science

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This paper deals with problematics of structure of Timed Automata models suitable for Model-Based Testing of automotive systems. Previous experiments, primarily focused on the environmental models, have shown that their structure does not significantly affect the coverage speed of testing process. However, similar questions regarding the observer part of the system model remained open. This paper analyzes those remaining questions and focuses on uncovering possible relation between an observer model structure and the quality of generated test sequences according to multiple criteria. Goal of presented experiments is to compare multiple modeling approaches and discover which one is most suitable for automotive systems.

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Section: Fig 1 Diagram Of the Mbt Process Using The Taster Toolmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The trunk control system model is similar to the common observer model used in previous experiments presented in [6] and is shown on Fig. 3.…”

Section: Fig 2 the Locking System Observer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Relation Between Test Coverage and Timed Automata Model Structure

Krejci

Sobotka

Novák

2021

Communications in Computer and Information Science

Self Cite

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Enhanced Adaptive Random Test Case Prioritization for Model-based Test Suites

Pospíšil¹,

Sobotka²,

Novák³

2020

ACTA POLYTECH HUNG

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Adaptive Random Prioritization is a Test Case Prioritization technique which orders test cases within a test suite with a goal of earlier fault detection using semi-random heuristics. Compared to other Test Case Prioritization methods, Adaptive Random Prioritization has only, an "average fault detection performance. However, it is less sensitive to some test suite features which negatively affect fault detection performance than other TCP techniques due to its semi-random nature. The article proposes an improved version of Adaptive Random Prioritization technique. The key idea behind the presented enhancement is to extend the test case selection process with additional information about control flow and change of test statements coverage, of a test suite. The enhancement replaces the original Test set distance function with a Multi-Criteria Decision-Making method. Validity of the proposed method is evaluated on data from six embedded systems. The evaluation criterion is fault detection performance expressed by Average Percentage of Faults Detection metric and Â12 statistic. The proposed improvement achieved better fault detection performance for all of the examined systems.

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Testing of Automotive Systems - Complex vs. Simple Environment Models

Cited by 2 publications

References 7 publications

Relation Between Test Coverage and Timed Automata Model Structure

Relation Between Test Coverage and Timed Automata Model Structure

Enhanced Adaptive Random Test Case Prioritization for Model-based Test Suites

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