Towards Formal Fault Injection for Safety Assessment of Automated Systems
2023 (English)In: Fifth International Workshop on Formal Methods for Autonomous Systems, 2023Conference paper, Published paper (Refereed)
Abstract [en]
Reasoning about safety, security, and other dependability attributes of autonomous systems is a challenge that needs to be addressed before the adoption of such systems in day-to-day life. Formal methods is a class of methods that mathematically reason about a system’s behavior. Thus, a correctness proof is sufficient to conclude the system’s dependability. However, these methods are usually applied to abstract models of the system, which might not fully represent the actual system. Fault injection, on the other hand, is a testing method to evaluate the dependability of systems. However, the amount of testing required to evaluate the system is rather large and often a problem. This vision paper introduces formal fault injection, a fusion of these two techniques throughout the development lifecycle to enhance the dependability of autonomous systems. We advocate for a more cohesive approach by identifying five areas of mutual support between formal methods and fault injection. By forging stronger ties between the two fields, we pave the way for developing safe and dependable autonomous systems. This paper delves into the integration’s potential and outlines future research avenues, addressing open challenges along the way.
Place, publisher, year, edition, pages
2023.
Keywords [en]
Fault injection, formal methods
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:ri:diva-67578OAI: oai:DiVA.org:ri-67578DiVA, id: diva2:1808636
Conference
International Workshop on Formal Methods for Autonomous Systems
Note
This work was partly supported by the VALU3S project, which has received funding from the ECSEL Joint Undertaking(JU) under grant agreement No 876852. The JU receives support from the European Union’s Horizon 2020 research andinnovation programme and Austria, Czech Republic, Germany, Ireland, Italy, Portugal, Spain, Sweden, Turkey. This work hasalso been partly financed by the CyReV project, which is funded by the VINNOVA FFI program – the Swedish GovernmentalAgency for Innovation Systems (Diary number: 2019-03071).
2023-10-312023-10-312023-11-01Bibliographically approved