Less Manual Work for Safety Engineers: Towards an Automated Safety Reasoning with Safety Patterns
| Autor: | Vivek Nigam, Yuri Gil Dantas, Antoaneta Kondeva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Logic in Computer Science Computer Science - Cryptography and Security Formal Languages and Automata Theory (cs.FL) Computer science Automotive industry System safety Computer Science - Formal Languages and Automata Theory Systems and Control (eess.SY) 02 engineering and technology Electrical Engineering and Systems Science - Systems and Control 020204 information systems Safety engineering FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) Automated reasoning Cruise control Logic programming business.industry 020206 networking & telecommunications Automation Logic in Computer Science (cs.LO) Software engineering business Cryptography and Security (cs.CR) |
| Popis: | The development of safety-critical systems requires the control of hazards that can potentially cause harm. To this end, safety engineers rely during the development phase on architectural solutions, called safety patterns, such as safety monitors, voters, and watchdogs. The goal of these patterns is to control (identified) faults that can trigger hazards. Safety patterns can control such faults by e.g., increasing the redundancy of the system. Currently, the reasoning of which pattern to use at which part of the target system to control which hazard is documented mostly in textual form or by means of models, such as GSN-models, with limited support for automation. This paper proposes the use of logic programming engines for the automated reasoning about system safety. We propose a domain-specific language for embedded system safety and specify as disjunctive logic programs reasoning principles used by safety engineers to deploy safety patterns, e.g., when to use safety monitors, or watchdogs. Our machinery enables two types of automated safety reasoning: (1) identification of which hazards can be controlled and which ones cannot be controlled by the existing safety patterns; and (2) automated recommendation of which patterns could be used at which place of the system to control potential hazards. Finally, we apply our machinery to two examples taken from the automotive domain: an adaptive cruise control system and a battery management system. In Proceedings ICLP 2020, arXiv:2009.09158 |
| Databáze: | OpenAIRE |
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