Complete Agent-driven Model-based System Testing for Autonomous Systems
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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing
GUI testing checks if a software system behaves as expected when users interact with its graphical interface, e.g., testing specific functionality or validating relevant use case scenarios. Currently, deciding what to te…
Language ModelingLanguage ModellingLarge Language ModelARIA - An Agentic Framework for Autonomous Testing of Infotainment Systems
Automotive infotainment validation still relies on manual testing, slow, costly, and incompatible with agile releases and OTA updates. Scripted automation only partly helps: it couples test logic to implementation, yield…
Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testing
When an autonomous agent behaves undesirably, including failure to complete a task, it can be difficult to determine whether the behavior is due to a systemic agent error, such as flaws in the model or policy, or an envi…
Cochise: A Reference Harness for Autonomous Penetration Testing
Recent work on LLM-driven autonomous penetration testing reports promising results, but existing systems often combine many architectural, prompting, and tool-integration choices, making it difficult to tell what is gain…
AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents
Autonomous agents that execute human tasks by controlling computers can enhance human productivity and application accessibility. However, progress in this field will be driven by realistic and reproducible benchmarks. W…
Benchmarking