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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.16876 |
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| _version_ | 1866909826513108992 |
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| author | Eris, Halit Wagner, Stefan |
| author_facet | Eris, Halit Wagner, Stefan |
| contents | Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing failures, tracing anomalies, and maintaining transparency, with current manual testing methods being inefficient and labor-intensive. This vision paper presents a methodology that integrates explainability, transparency, and interpretability into V&V processes. We propose refining V&V requirements through literature reviews and stakeholder input, generating explainable test scenarios via large language models (LLMs), and enabling real-time validation in simulation environments. Our framework includes test oracle, explanation generation, and a test chatbot, with empirical studies planned to evaluate improvements in diagnostic efficiency and transparency. Our goal is to streamline V&V, reduce resources, and build user trust in autonomous technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems Eris, Halit Wagner, Stefan Software Engineering Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing failures, tracing anomalies, and maintaining transparency, with current manual testing methods being inefficient and labor-intensive. This vision paper presents a methodology that integrates explainability, transparency, and interpretability into V&V processes. We propose refining V&V requirements through literature reviews and stakeholder input, generating explainable test scenarios via large language models (LLMs), and enabling real-time validation in simulation environments. Our framework includes test oracle, explanation generation, and a test chatbot, with empirical studies planned to evaluate improvements in diagnostic efficiency and transparency. Our goal is to streamline V&V, reduce resources, and build user trust in autonomous technologies. |
| title | Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2506.16876 |