MIDES: A Tool for Supervisor Synthesis via Active Learning

Farooqui, Ashfaq; Hagebring, Fredrik; Fabian, Martin

doi:10.1109/case49439.2021.9551435

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…2. Learning the behavior of the faulty system via model learning/learning-based testing: Model learning [13,42,26] seeks to devise techniques for acquiring discrete formal models of systems by observing or interacting with them. These techniques engage in iterative testing of the system, or its simulation, to progressively learn the behavior.…”

Section: Integrating Formal Methods and Fault Injectionmentioning

confidence: 99%

“…Existing model learning tools, already applied in select industrial contexts [14,19], provide interfaces external systems. However, an investigation is warranted to devise methods for introducing fault models into these tools and subsequently evaluating their utility.…”

Section: Integrating Formal Methods and Fault Injectionmentioning

confidence: 99%

“…These techniques engage in iterative testing of the system, or its simulation, to progressively learn the behavior. Model learning techniques are often applied in combination with other tools and methodologies: model checking [8] for model verification, testing methods [26] to rigorously assess system behavior, and supervisory synthesis [13] to derive supervisory controllers for system control.…”

Section: Integrating Formal Methods and Fault Injectionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Formal Fault Injection for Safety Assessment of Automated Systems

Farooqui,

Sangchoolie

2023

Electron. Proc. Theor. Comput. Sci.

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Reasoning about safety, security, and other dependability attributes of autonomous systems is a challenge that needs to be addressed before the adoption of such systems in day-to-day life. Formal methods is a class of methods that mathematically reason about a system's behavior. Thus, a correctness proof is sufficient to conclude the system's dependability. However, these methods are usually applied to abstract models of the system, which might not fully represent the actual system. Fault injection, on the other hand, is a testing method to evaluate the dependability of systems. However, the amount of testing required to evaluate the system is rather large and often a problem. This vision paper introduces formal fault injection, a fusion of these two techniques throughout the development lifecycle to enhance the dependability of autonomous systems. We advocate for a more cohesive approach by identifying five areas of mutual support between formal methods and fault injection. By forging stronger ties between the two fields, we pave the way for developing safe and dependable autonomous systems. This paper delves into the integration's potential and outlines future research avenues, addressing open challenges along the way.

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Section: Integrating Formal Methods and Fault Injectionmentioning

confidence: 99%

Section: Integrating Formal Methods and Fault Injectionmentioning

confidence: 99%