Uncertainty, Modeling and Safety Assurance: Towards a Unified Framework

Chećhik, Marsha; Kokaly, Sahar; Rahimi, Mona; Salay, Rick; Viger, Torin

doi:10.1007/978-3-030-41600-3_2

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…They use different approaches, such as feature or variability models [39,94], Fuzzy set theory [5,20,30,115], Fuzzy branching temporal logic [107,108], stochastic Petri nets [35], or even Machine Learning techniques (Model Trees Learning) [29]. Finally, another paper of this group [17] uses partial models and probabilities to deal with uncertain behavior in assurance cases in the Automotive domain, while partial models are used in [67] to handle uncertain environments.…”

Section: Types Of Uncertainty Addressedmentioning

confidence: 99%

Uncertainty representation in software models: a survey

et al. 2021

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This paper provides a comprehensive overview and analysis of research work on how uncertainty is currently represented in software models. The survey presents the definitions and current research status of different proposals for addressing uncertainty modeling, and introduces a classification framework that allows to compare and classify existing proposals, analyse their current status and identify new trends. In addition, we discuss possible future research directions, opportunities and challenges.A fundamental characteristic of software models is their ability to represent the relevant characteristics of the system under study, at the appropriate level of abstraction. Software models were initially conceived to design and develop general Information Technology (IT) systems, such as financial applications, enterprise databases or component-based systems, and have proven to be excellent artefacts for representing the basic structure and

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Section: Types Of Uncertainty Addressedmentioning

confidence: 99%

Uncertainty representation in software models: a survey

et al. 2021

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“…Motivating Example. Consider the Lane Management System (LMS) system outlined in [4]. LMS can be thought of as a product line with several features, including: Lane Departure Warning System (LDWS), Audio warning (Audio), and Visual warning (Visual).…”

Section: Introductionmentioning

confidence: 99%

Towards Certified Analysis of Software Product Line Safety Cases

Shahin¹,

Kokaly²,

Chećhik³

2021

Preprint

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Safety-critical software systems are in many cases designed and implemented as families of products, usually referred to as Software Product Lines (SPLs). Products within an SPL vary from each other in terms of which features they include. Applying existing analysis techniques to SPLs and their safety cases is usually challenging because of the potentially exponential number of products with respect to the number of supported features. In this paper, we present a methodology and infrastructure for certified lifting of existing single-product safety analyses to product lines. To ensure certified safety of our infrastructure, we implement it in an interactive theorem prover, including formal definitions, lemmas, correctness criteria theorems, and proofs. We apply this infrastructure to formalize and lift a Change Impact Assessment (CIA) algorithm. We present a formal definition of the lifted algorithm, outline its correctness proof (with the full machine-checked proof available online), and discuss its implementation within a model management framework.

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