Complete Agent-driven Model-based System Testing for Autonomous Systems
This addresses critical safety and efficiency problems for developers and regulators in automotive, avionic, and railway domains, though it appears incremental as it builds on existing standards and methods.
The paper tackles the infeasibility of verification and validation for complex autonomous transportation systems by proposing a novel testing approach that combines formal proofs at the module level with system-level tests in simulated and target environments, using an agent-based method to optimize execution.
In this position paper, a novel approach to testing complex autonomous transportation systems (ATS) in the automotive, avionic, and railway domains is described. It is intended to mitigate some of the most critical problems regarding verification and validation (V&V) effort for ATS. V&V is known to become infeasible for complex ATS, when using conventional methods only. The approach advocated here uses complete testing methods on the module level, because these establish formal proofs for the logical correctness of the software. Having established logical correctness, system-level tests are performed in simulated cloud environments and on the target system. To give evidence that 'sufficiently many' system tests have been performed with the target system, a formally justified coverage criterion is introduced. To optimise the execution of very large system test suites, we advocate an online testing approach where multiple tests are executed in parallel, and test steps are identified on-the-fly. The coordination and optimisation of these executions is achieved by an agent-based approach. Each aspect of the testing approach advocated here is shown to either be consistent with existing standards for development and V&V of safety-critical transportation systems, or it is justified why it should become acceptable in future revisions of the applicable standards.