What Good are Models?

Lee, Edward A.; Sirjani, Marjan

doi:10.1007/978-3-030-02146-7_1

Cited by 26 publications

(12 citation statements)

References 38 publications

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“…For controller design and synthesis, control engineers perform model validation experiments to assess how well a model of the process, they want to control, complies with the real world [37]. Likewise, the formal model associated with an FMAC (extension) has to be accompanied by an argument (potentially based on experiments using simulation and test [9]) that this model faithfully abstracts from and predicts [21] the implemented controller (i.e., the potentially distributed embedded system) and the surrounding plant. Isabelle/SACM allows us to enhance arguments accordingly.…”

Section: Discussionmentioning

confidence: 99%

Evolution of Formal Model-Based Assurance Cases for Autonomous Robots

Gleirscher

Foster

Nemouchi

2019

Software Engineering and Formal Methods

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An assurance case should carry sufficient evidence for a compelling argument that a system fulfils its guarantees under specific environmental assumptions. Assurance cases are often subject of maintenance, evolution, and reuse. In this paper, we demonstrate how evidence of an assurance case can be formalised, and how an assurance case can be refined using this formalisation to increase argument confidence and to react to changing operational needs. Moreover, we propose two argument patterns for construction and extension and we implement these patterns using the generic proof assistant Isabelle. We illustrate our approach for an autonomous mobile ground robot. Finally, we relate our approach to international standards (e.g. DO-178C, ISO 26262) recommending the delivery and maintenance of assurance cases.

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Section: Discussionmentioning

confidence: 99%

Evolution of Formal Model-Based Assurance Cases for Autonomous Robots

Gleirscher

Foster

Nemouchi

2019

Software Engineering and Formal Methods

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“…In [45], Lee and Sirjani distinguish what they call scientific models, which are intended to reflect the behavior of preexisting systems, from what they call engineering models, which are intended to specify the behavior of a system to be built. They point out that it is important to recognize whether a model is to be used for a scientific or an engineering purpose.…”

Section: E Virtual Prototypingmentioning

confidence: 99%

Programmable Logic Controllers in the Context of Industry 4.0

Sehr

Lohstroh

Weber

et al. 2021

IEEE Trans. Ind. Inf.

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Programmable Logic Controllers (PLCs) are an established platform, widely used throughout industrial automation but poorly understood among researchers. This paper gives an overview of the state of the practice, explaining why this settled technology persists throughout industry and presenting a critical analysis of the strengths and weaknesses of the dominant programming styles for today's PLC-based automation systems. We describe the software execution patterns that are standardized loosely in IEC 61131-3. We identify opportunities for improvements that would enable increasingly complex industrial automation applications while strengthening safety and reliability. Specifically, we propose deterministic, distributed programming models that embrace explicit timing, event-triggered computation, and improved security.

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“…Nevertheless, as statistician George Box said "all models are wrong but some are useful" [19]: we must evaluate a model not upon how "correct" it is, or how much detail it contains, but on how informative it is. According to Lee and Sirjani [117], the antidote is not to abandon the use of models, but to recognise their inherent limitations and strengths, and apply them intelligently to reasoning about a specific problem. This means selecting appropriate modelling paradigms that enable specification of behaviour at a sufficiently detailed level of abstraction, and using the resulting models to guide the engineering process.…”

Section: Expressivitymentioning

confidence: 99%

New Opportunities for Integrated Formal Methods

2019

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Formal methods have provided approaches for investigating software engineering fundamentals and also have high potential to improve current practices in dependability assurance. In this article, we summarise known strengths and weaknesses of formal methods. From the perspective of the assurance of robots and autonomous systems (RAS), we highlight new opportunities for integrated formal methods and identify threats to the adoption of such methods. Based on these opportunities and threats, we develop an agenda for fundamental and empirical research on integrated formal methods and for successful transfer of validated research to RAS assurance. Furthermore, we outline our expectations on useful outcomes of such an agenda. AcronymsAI artificial intelligence ASIL automotive safety integrity level DA dependability assurance DAL design assurance level DSE dependable systems engineering DSL domain-specific language FDA Food and Drug Administration FI formal inspection FM formal method HCI human-computer interaction iFM integrated formal method IT information technology MBD model-based development MDE model-driven engineering ML machine learning RAS robots and autonomous systems RCA root cause analysis RE requirements engineering SACM Structured Assurance Case Meta-model SC systematic capability SIL safety integrity level SMT Satisfiability Modulo Theory SWOT strengths, weaknesses, opportunities, and threats SysML Systems Modelling Language UML Unified Modelling Language UTP Unifying Theories of ProgrammingWe view robots and autonomous systems as both dependable systems and highly automated machines capable of achieving a variety of complex tasks in support of humans. We can consider such systems by looking at four layers: the plant or process composed of the operational environment and the machine; the machine itself; the machine's controller, and the software embedded into this controller. Based on these layers, we treat "embedded system" and "embedded software" as synonyms. Machine, controller, and software can all be distributed.By dependable systems engineering, we refer to error-avoidance and error-detection activities in control system and embedded software development (e.g. according to the V-model). Avizienis et al.[7] devised a comprehensive terminology and an overview of the assessment and handling of a variety of faults, errors, and failures. For critical systems, such activities are expected to be explicit (e.g. traceable, documented), to employ best practices (e.g. design patterns), and to be driven by reasonably qualified personnel (e.g. well-trained and experienced engineers or programmers).The need for dependability often arises from the embedding of software into a cyber-physical context (i.e., an electronic execution platform, a physical process to be controlled, and other systems or human users to interact with). Dependability assurance (DA), or assurance for short, encompasses the usually cross-disciplinary task of providing evidence for an assurance case (e.g. safety, security, reliability) for a sys...

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What Good are Models?

Cited by 26 publications

References 38 publications

Evolution of Formal Model-Based Assurance Cases for Autonomous Robots

Evolution of Formal Model-Based Assurance Cases for Autonomous Robots

Programmable Logic Controllers in the Context of Industry 4.0

New Opportunities for Integrated Formal Methods

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