Model-Checking based Verification approach for advanced Industrial Automation solutions

Mazzolini, Mauro; Brusaferri, Alessandro; Carpanzano, Emanuele

doi:10.1109/etfa.2010.5641209

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…It has been previously used to detect faults in Simulink models [14,21,5,31] by showing that a path leading to an error (e.g., an assertion or a runtime error) is reachable, or by maximizing structural coverage (e.g., by executing as many paths as possible in a model). To solve the reachability problem or to achieve high coverage, these techniques often extract constraints from the underlying Simulink models and feed the constraints into some constraint solver or SAT solver.…”

Section: Related Workmentioning

confidence: 99%

Automated test suite generation for time-continuous simulink models

Matinnejad

Nejati

Briand

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Interdisciplinary domains such as Cyber Physical Systems (CPSs) seek approaches that incorporate different modeling needs and usages. Specifically, the Simulink modeling platform greatly appeals to CPS engineers due to its seamless support for simulation and code generation. In this paper, we propose a test generation approach that is applicable to Simulink models built for both purposes of simulation and code generation. We define test inputs and outputs as signals that capture evolution of values over time. Our test generation approach is implemented as a meta-heuristic search algorithm and is guided to produce test outputs with diverse shapes according to our proposed notion of diversity. Our evaluation, performed on industrial and public domain models, demonstrates that: (1) In contrast to the existing tools for testing Simulink models that are only applicable to a subset of code generation models, our approach is applicable to both code generation and simulation Simulink models. (2) Our new notion of diversity for output signals outperforms random baseline testing and an existing notion of signal diversity in revealing faults in Simulink models. (3) The fault revealing ability of our test generation approach outperforms that of the Simulink Design Verifier, the only testing toolbox for Simulink.

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Section: Related Workmentioning

confidence: 99%

Automated test suite generation for time-continuous simulink models

Matinnejad

Nejati

Briand

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

View full text Add to dashboard Cite

show abstract

“…All of the studies that use FBDs are performed after 2007, and a great portion of these studies are performed after 2010. On the other hand, sequential function charts were mostly used between 2000 and 2005, they seem to be not preferred for more recent model check- CFCs [110] Simulink and data/state flowcharts [60,78] Other [107] (REMES) [45] (LUSTRE), [105] (tMPSG) [111] S (VKRC) ing studies. Another interesting point is the lack of Timed Automata studies using Petri nets.…”

Section: Classification Of Practicesmentioning

confidence: 98%

“…Mazzolini et al [78] used MATLAB state flowcharts and perform verification of a shoe manufacturing plant model with Simulink Design Verifier in 35 s where the model contained 28 states, 34 transitions, and 21 variables. Jimenez-Fraustro and Rutten [60] also use Simulink in verifying PLC programs modelled using a data-flow language SIGNAL [71].…”

Section: Other Approaches That Use Model Checkingmentioning

confidence: 99%

An overview of model checking practices on verification of PLC software

et al. 2014

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Programmable logic controllers (PLCs) are heavily used in industrial control systems, because of their high capacity of simultaneous input/output processing capabilities. Characteristically, PLC systems are used in mission critical systems, and PLC software needs to conform real-time constraints in order to work properly. Since PLC programming requires mastering low-level instructions or assembly like languages, an important step in PLC software production is modelling using a formal approach like Petri nets or automata. Afterward, PLC software is produced semiautomatically from the model and refined iteratively. Model checking, on the other hand, is a well-known software verification approach, where typically a set of timed properties are verified by exploring the transition system produced from the software model at hand. Naturally, model checking is applied in a variety of ways to verify the correctness of PLC-based software. In this paper, we provide a broad view about the difficulties that are encountered during the model checking process applied at the verification phase of PLC software production. We classify the approaches from two different perCommunicated by Prof. Einar Broch Johnsen and Luigia Petre. spectives: first, the model checking approach/tool used in the verification process, and second, the software model/source code and its transformation to model checker's specification language. In a nutshell, we have mainly examined SPIN, SMV, and UPPAAL-based model checking activities and model construction using Instruction Lists (and alike), Function Block Diagrams, and Petri nets/automata-based model construction activities. As a result of our studies, we provide a comparison among the studies in the literature regarding various aspects like their application areas, performance considerations, and model checking processes. Our survey can be used to provide guidance for the scholars and practitioners planning to integrate model checking to PLC-based software verification activities.

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“…to test specific scenarios against exhaustive verification offered by model checking. The model-checking methods proposed in [7] and [8] lack the support of modelling framework and therefore, are restricted to specific architecture or scenarios. Vogel-Heuser et al [9] presented a component oriented modelling approach that captures the timing requirements and specifications to verify response time in NAS.…”

Section: Introductionmentioning

confidence: 99%

Verifying Response Times in Networked Automation Systems Using Jitter Bounds

Srinivasan

Buonopane

Ramaswamy

et al. 2014

2014 IEEE International Symposium on Software Reliability Engineering Workshops

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Abstract-Networked Automation Systems (NAS) have to meet stringent response time during operation. Verifying response time of automation is an important step during design phase before deployment. Timing discrepancies due to hardware, software and communication components of NAS affect the response time. This investigation uses model templates for verifying the response time in NAS. First, jitter bounds model the timing fluctuations of NAS components. These jitter bounds are the inputs to model templates that are formal models of timing fluctuations. The model templates are atomic action patterns composed of three composition operators-sequential, alternative, and parallel and embedded in time wrapper that specifies clock driven activation conditions. Model templates in conjunction with formal model of technical process offer an easier way to verify the response time. The investigation demonstrates the proposed verification method using an industrial steam boiler with typical NAS components in plant floor.

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Model-Checking based Verification approach for advanced Industrial Automation solutions

Cited by 19 publications

References 11 publications

Automated test suite generation for time-continuous simulink models

Automated test suite generation for time-continuous simulink models

An overview of model checking practices on verification of PLC software

Verifying Response Times in Networked Automation Systems Using Jitter Bounds

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