Validation Of Safety-critical Systems Research Articles

Introducing plant features to model-based testing of programmable controllers in automation systems

This paper proposes a model-based test generation approach for programmable controllers that aims at reducing the length of a test sequence by applying plant features. The proposed approach does not require detailed or full knowledge of the plant behavior of a system under test, but it can achieve a remarkable reduction with simple plant features. As a result, the obtained test sequence can be significantly shorter than ones generated by complete testing methods; and meanwhile, it still reaches full coverage of the nominal behavior of the system under test. This makes it feasible to test large-scale systems, or to serve as an early test in the validation of safety critical systems. The proposed approach has been illustrated on a large-scale case study.

A Formal Approach for Scalable Simulation of Gastric ICC Electrophysiology.

Efficient and accurate organ models are crucial for closed-loop validation of implantable medical devices. This paper investigates bio-electric slow wave modeling of the stomach, so that gastric electrical stimulator (GES) can be validated and verified prior to implantation. In particular, we consider high-fidelity, scalable, and efficient modeling of the pacemaker, Interstitial cells of Cajal (ICC), based on the formal hybrid input output automata (HIOA) framework. Our work is founded in formal methods, a collection of mathematically sound techniques originating in computer science for the design and validation of safety-critical systems. We modeled each ICC cell using an HIOA. We also introduce an HIOA path model to capture the electrical propagation delay between cells in a network. The resultant network of ICC cells can simulate normal and diseased action potential propagation patterns, making it useful for device validation. The simulated slow wave of a single ICC cell had high correlation ( ≈ 0.9) with the corresponding biophysical models. The proposed model is able to simulate the slow wave activity of a network of ICC cells with high-fidelity for device validation. The proposed HIOA model is significantly more efficient than the corresponding biophysical models, scales to larger networks of ICC cells, and is capable of simulating varying propagation patterns. This has the potential to enable verification and validation of implantable GESs in closed-loop with gastrointestinal models in the future.

Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry

Deep neural networks (DNNs) will emerge as a cornerstone in automotive software engineering. However, developing systems with DNNs introduces novel challenges for safety assessments. This paper reviews the state-of-the-art in verification and validation of safety-critical systems that rely on machine learning. Furthermore, we report from a workshop series on DNNs for perception with automotive experts in Sweden, confirming that ISO 26262 largely contravenes the nature of DNNs. We recommend aerospace-to-automotive knowledge transfer and systems-based safety approaches, for example, safety cage architectures and simulated system test cases.