3 research outputs found
Systematic Model-based Design Assurance and Property-based Fault Injection for Safety Critical Digital Systems
With advances in sensing, wireless communications, computing, control, and automation technologies, we are witnessing the rapid uptake of Cyber-Physical Systems across many applications including connected vehicles, healthcare, energy, manufacturing, smart homes etc. Many of these applications are safety-critical in nature and they depend on the correct and safe execution of software and hardware that are intrinsically subject to faults. These faults can be design faults (Software Faults, Specification faults, etc.) or physically occurring faults (hardware failures, Single-event-upsets, etc.). Both types of faults must be addressed during the design and development of these critical systems. Several safety-critical industries have widely adopted Model-Based Engineering paradigms to manage the design assurance processes of these complex CPSs. This thesis studies the application of IEC 61508 compliant model-based design assurance methodology on a representative safety-critical digital architecture targeted for the Nuclear power generation facilities. The study presents detailed experiences and results to demonstrate the benefits of Model testing in finding design flaws and its relevance to subsequent verification steps in the workflow. Additionally, to study the impact of physical faults on the digital architecture we develop a novel property-based fault injection method that overcomes few deficiencies of traditional fault injection methods. The model-based fault injection approach presented here guarantees high efficiency and near-exhaustive input/state/fault space coverage, by utilizing formal model checking principles to identify fault activation conditions and prove the fault tolerance features. The fault injection framework facilitates automated integration of fault saboteurs throughout the model to enable exhaustive fault location coverage in the model
Application of Orthogonal Defect Classification for Software Reliability Analysis
The modernization of existing and new nuclear power plants with digital
instrumentation and control systems (DI&C) is a recent and highly trending
topic. However, there lacks strong consensus on best-estimate reliability
methodologies by both the United States (U.S.) Nuclear Regulatory Commission
(NRC) and the industry. In this work, we develop an approach called
Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to
quantify probabilities of various software failure modes in a DI&C system. The
method utilizes accepted industry methodologies for quality assurance that are
verified by experimental evidence. In essence, the approach combines a semantic
failure classification model with a reliability growth model to predict the
probability of failure modes of a software system. A case study was conducted
on a representative I&C platform (ChibiOS) running a smart sensor acquisition
software developed by Virginia Commonwealth University (VCU). The testing and
evidence collection guidance in ORCAS was applied, and defects were uncovered
in the software. Qualitative evidence, such as modified condition decision
coverage, was used to gauge the completeness and trustworthiness of the
assessment while quantitative evidence was used to determine the software
failure probabilities. The reliability of the software was then estimated and
compared to existing operational data of the sensor device. It is demonstrated
that by using ORCAS, a semantic reasoning framework can be developed to justify
if the software is reliable (or unreliable) while still leveraging the strength
of the existing methods.Comment: 12 pages, 3 figures, 4 tables, conference transaction presented at
Probabilistic Safety Assessment and Management 202