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Complete Agent-driven Model-based System Testing for Autonomous Systems -- Technical Report

Authors: Eder, Kerstin; Huang, Wen-ling; Peleska, Jan;

Complete Agent-driven Model-based System Testing for Autonomous Systems -- Technical Report

Abstract

This technical report is an extended version of the authors' submission to FMAS~2021. It is intended as a position paper, where we present a novel approach to testing complex autonomous transportation systems (ATS) in the automotive, avionic, and railway domains. It is well-suited to overcome the problems of verification and validation (V&V) effort which is known to become infeasible for complex ATS, when trying to perform V&V with conventional methods. The approach advocated here uses complete testing methods on 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 be consistent with existing standards for development and V&V of safety-critical transportation systems, or it is justified why they should become acceptable in future revisions of the applicable standards.

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Keywords

Agent-based testing, System testing, Autonomous transportation systems, Complete test methods, Module testing

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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