ATTEST: Automating the review and update of assurance case arguments
Faiz Ul Muram, Muhammad Atif Javed
Abstract
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).