ATTEST: Automating the review and update of assurance case arguments
2023 (English)In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 134, article id 102781Article in journal (Refereed) Published
Abstract [en]
The assurance case arguments are created to demonstrate acceptable system safety and/or security. In this regard, a series of propositions expressed by natural language statements (claims) are broken down into sub-claims representing a logical chain of reasoning until the corresponding evidence is obtained. The review and update of assurance arguments for aligning with the process and product counterparts used for their construction are essential tasks. These tasks are perceived as challenging but can be efficiently supported by using Natural Language Processing (NLP). To date, however, the published studies on assurance cases have not leveraged the NLP. Accordingly, this paper presents our NLP-based assurance framework called ATTEST. At first, the text preprocessing is carried out by using NLP tasks. The rules are created, in which both syntactic and semantic features are captured. The former is captured by using NLP tasks, while the latter is captured by the internal structure of models as well as the mappings across them. The created rules are triggered for argument comprehension, well-formedness, sufficiency checks, and identifying defeaters and counter-evidence selection. Besides the process, product, and assurance case models produced during the design and development phase, the operational data is gathered from the configured simulation environments and used for identifying problems as well as the measures for resolving them. Finally, the affected parts of assurance case models are highlighted and the underlying reasoning for their adaptation is presented. The applicability of the proposed framework is demonstrated by reviewing and updating assurance cases constructed for vehicular Accelerator Control System (ACS) with Electronic Throttle Control (ETC). © 2022 The Author(s)
Place, publisher, year, edition, pages
Elsevier B.V. , 2023. Vol. 134, article id 102781
Keywords [en]
Assurance cases, Evolution and maintenance, GSN, NLP, Review, Up-to-date assurance cases, Natural language processing systems, Product design, Assurance case, Language processing, Language statements, Natural language processing, Natural languages, System safety, Up-to-date assurance case, Semantics
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:ri:diva-61358DOI: 10.1016/j.sysarc.2022.102781Scopus ID: 2-s2.0-85142318390OAI: oai:DiVA.org:ri-61358DiVA, id: diva2:1717613
Note
Funding details: Stiftelsen för Strategisk Forskning, SSF, 20190335; Funding text 1: This work is supported by 1) ESCAPE (Efficient and effective functional safety for complex autonomous production systems) project funded by Vinnova, 2) FiC (Future factories in the Cloud) project funded by SSF (Swedish Foundation for Strategic Research) and 3) KK-stiftelsen programme “associate senior lecturer in models for smarter systems” (reference number 20190335 ). The second author has also participated during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme.
2022-12-092022-12-092022-12-09Bibliographically approved