2 research outputs found
Measuring Confidence of Assurance Cases in Safety-Critical Domains
Evaluation of assurance cases typically requires certifiersâ domain knowledge and experience, and, as such, most software certification has been conducted manually. Given the advancement in uncertainty theories and software traceability, we envision that these technologies can synergistically be combined and leveraged to offer some degree of automation to improve the certifiersâ capability to perform software certification. To this end, we present DS4AC, a novel confidence calculation framework that 1) applies the Dempster-Shafer theory to calculate the confidence between a parent claim and its children claims; and 2) uses the vector space model to evaluate the confidence for the evidence items using traceability information. We illustrate our approach on two different applications, where safety is the key property of interest for both systems. In both cases, we use the Goal Structuring Notation to represent the respective assurance cases and provide proof of concept results that demonstrate the DS4AC framework can automate portions of the evaluation of assurance cases, thereby reducing the burden of manual certification process
Design-time detection of physical-unit changes in product lines
Software product lines evolve over time, both as new products are added to the product line and as existing products are updated. This evolution creates unintended as well as planned changes to Systems. A persistent problem is that unintended changes are hard to detect. Often they are not discovered until testing or operations. Late discovery is a problem especially in safety-critical, cyberphysical product lines such as avionics, pacemakers, and smart-braking systems, where unintended
changes may lead to accidents.
This thesis proposes an approach and a prototype tool to detect unintended changes earlier in development of a new product in the product line. The capability to detect potentially risky, unintended changes at the design stage is beneficial because repair is easier, less costly, and safer in design than when detection is delayed to testing or operations.
The Product Line Change Detector (PLCD) introduced here analyzes productsâ SysML block and parametric diagrams, which are typical project artifacts for cyber-physical systems, in order to detect problematic, unintended changes. The PLCD software automatically detects potential change-related issues, ranks them in terms of severity using the productsâ safety-analysis artifacts, and reports them to developers in a graphical format. Developers select and fix the reported issues with the assistance of the toolâs displays, with the tool recording the fixes and updating the SysML diagrams accordingly.
The evaluation of PLCDâs performance and capabilities uses three product lines, extended from cyber-physical systems in the literature: NASA astronaut jetpack, vehicle dynamics, and low-earth satellite. The evaluation focuses on unintended changes that cause physical unit inconsistencies, such as between meters and feet, since those may lead to accidents in cyber-physical product lines. The evaluation results show that PLCD successfully detects such unintended changes both in a single product and between products in a software product line