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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.05112 |
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| _version_ | 1866912573251649536 |
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| author | Zyberaj, Denesa Mazur, Lukasz Petrovic, Nenad Verma, Pankhuri Hirmer, Pascal Slama, Dirk Cheng, Xiangwei Knoll, Alois |
| author_facet | Zyberaj, Denesa Mazur, Lukasz Petrovic, Nenad Verma, Pankhuri Hirmer, Pascal Slama, Dirk Cheng, Xiangwei Knoll, Alois |
| contents | This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed within the digital.auto playground, an open and vendor-neutral environment designed to facilitate rapid validation of software-defined vehicle functionalities. We evaluate our approach using the Child Presence Detection System use case, demonstrating substantial reductions in manual test specification effort and rapid execution of generated tests. Despite significant automation, the generation of test cases and test scripts still requires manual intervention due to current limitations in the GenAI pipeline and constraints of the digital.auto platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenAI-based test case generation and execution in SDV platform Zyberaj, Denesa Mazur, Lukasz Petrovic, Nenad Verma, Pankhuri Hirmer, Pascal Slama, Dirk Cheng, Xiangwei Knoll, Alois Software Engineering Artificial Intelligence This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed within the digital.auto playground, an open and vendor-neutral environment designed to facilitate rapid validation of software-defined vehicle functionalities. We evaluate our approach using the Child Presence Detection System use case, demonstrating substantial reductions in manual test specification effort and rapid execution of generated tests. Despite significant automation, the generation of test cases and test scripts still requires manual intervention due to current limitations in the GenAI pipeline and constraints of the digital.auto platform. |
| title | GenAI-based test case generation and execution in SDV platform |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05112 |