Towards Dynamic Dependable Systems through Evidence-Based Continuous Certification

Abstract

International audienceFuture cyber-physical systems are expected to be dynamic, evolving while already being deployed. Frequent updates of software components are likely to become the norm even for safety-critical systems. In this setting, a full re-certification before each software update might delay important updates that fix previous bugs, or security or safety issues. Here we propose a vision addressing this challenge, namely through the evidence-based continuous supervision and certification of software variants in the field. The idea is to run both old and new variants of component software inside the same system, together with a supervising instance that monitors their behavior. Updated variants are phased into operation after sufficient evidence for correct behavior has been collected. The variants are required to explicate their decisions in a logical language, enabling the supervisor to reason about these decisions and to identify inconsistencies. To resolve contradictory information, the supervisor can run a component analysis to identify potentially faulty components on the basis of previously observed behavior, and can trigger micro-experiments which plan and execute system behavior specifically aimed at reducing uncertainty. We spell out our overall vision, and provide a first formalization of the different components and their interplay. In order to provide efficient supervisor reasoning as well as automatic verification of supervisor properties we introduce SupERLog, a logic specifically designed to this end