Generation of Failure Models through Automata Learning

Kunze, Sebastian; Mostowski, Wojciech; Mousavi, Mohammad Reza; Varshosaz, Mahsa

doi:10.1109/wasa.2016.7

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…Though such research indicates the use of active automata learning for real-life systems, challenges exist to broaden its impact for practical use [24,42,43]. There are very limited examples on the use of active automata learning in an automotive context [34,54] and it is yet to find its place in automotive software development. To the best of our knowledge, active automata learning has not been used previously to learn behavioural models from automotive software implemented in MATLAB/Simulink, a common model-based development tool within the automotive industry.…”

Section: Related Workmentioning

confidence: 99%

Automatically learning formal models

Selvaraj

Farooqui

Panahandeh

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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The correctness of autonomous driving software is of utmost importance as incorrect behaviour may have catastrophic consequences. Though formal model-based engineering techniques can help guarantee correctness, challenges exist in widespread industrial adoption. One among them is the model construction problem. Manual construction of formal models is expensive, error-prone, and intractable for large systems. Automating model construction would be a great enabler for the use of formal methods to guarantee software correctness and thereby for safe deployment of autonomous vehicles. Such automated techniques can be beneficial in software design, re-engineering, and reverse engineering. In this industrial case study, we apply active learning techniques to obtain formal models from an existing autonomous driving software (in development) implemented in MATLAB. We demonstrate the feasibility of active automata learning algorithms for automotive industrial use. Furthermore, we discuss the practical challenges in applying automata learning and possible directions for integrating automata learning into automotive software development workflow. CCS CONCEPTS • Software and its engineering → Software design engineering; Software development methods; Formal software verification; Software reverse engineering; • Computing methodologies → Model development and analysis; Machine learning; • Theory of computation → Models of computation; Formal languages and automata theory.

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Section: Related Workmentioning

confidence: 99%

Automatically learning formal models

Selvaraj

Farooqui

Panahandeh

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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“…1 specify them all in QuickCheck with just one common postcondition (ls. [5][6]. It states that for all operations (Call), the actual return value (Res) should be equal to the specified one (return_value).…”

Section: Quickcheck Model In Erlangmentioning

confidence: 99%

Model-based fault injection for testing gray-box systems

Mostowski

2019

Journal of Logical and Algebraic Methods in Programming

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“…3.3 also allows us to test implementation against mutations of sub-components to test for fault tolerance. This is part of on-going research [4,9], here we exemplify the idea and show the capabilities of QuickCheck to guide test generation to trigger a hidden fault.…”

Section: Faults Deviations and Consequence Testingmentioning

confidence: 99%

Modelling of Autosar Libraries for Large Scale Testing

Mostowski

Arts

Hughes

2017

Electron. Proc. Theor. Comput. Sci.

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We demonstrate a specific method and technology for model-based testing of large software projects with the QuickCheck tool using property-based specifications. Our specifications are very precise, state-full models of the software under test (SUT). In our approach we define (a) formal descriptions of valid function call sequences (public API), (b) postconditions that check the validity of each call, and (c) call-out specifications that define and validate external system interactions (SUT calling external API). The QuickCheck tool automatically generates and executes tests from these specifications. Commercially, this method and tool have been used to test large parts of the industrially developed automotive libraries based on the Autosar standard. In this paper, we exemplify our approach with a circular buffer specified by Autosar, to demonstrate the capabilities of the model-based testing method of QuickCheck. Our example is small compared to the commercial QuickCheck models, but faithfully addresses many of the same challenges.

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Generation of Failure Models through Automata Learning

Cited by 8 publications

References 6 publications

Automatically learning formal models

Automatically learning formal models

Model-based fault injection for testing gray-box systems

Modelling of Autosar Libraries for Large Scale Testing

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