DeepTest Tool Competition 2026: Benchmarking an LLM-Based Automotive Assistant
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913030007160832 |
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| author | Sorokin, Lev Vasilev, Ivan Pasini, Samuele |
| author_facet | Sorokin, Lev Vasilev, Ivan Pasini, Samuele |
| contents | This report summarizes the results of the first edition of the Large Language Model (LLM) Testing competition, held as part of the DeepTest workshop at ICSE 2026. Four tools competed in benchmarking an LLM-based car manual information retrieval application, with the objective of identifying user inputs for which the system fails to appropriately mention warnings contained in the manual. The testing solutions were evaluated based on their effectiveness in exposing failures and the diversity of the discovered failure-revealing tests. We report on the experimental methodology, the competitors, and the results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12615 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepTest Tool Competition 2026: Benchmarking an LLM-Based Automotive Assistant Sorokin, Lev Vasilev, Ivan Pasini, Samuele Artificial Intelligence This report summarizes the results of the first edition of the Large Language Model (LLM) Testing competition, held as part of the DeepTest workshop at ICSE 2026. Four tools competed in benchmarking an LLM-based car manual information retrieval application, with the objective of identifying user inputs for which the system fails to appropriately mention warnings contained in the manual. The testing solutions were evaluated based on their effectiveness in exposing failures and the diversity of the discovered failure-revealing tests. We report on the experimental methodology, the competitors, and the results. |
| title | DeepTest Tool Competition 2026: Benchmarking an LLM-Based Automotive Assistant |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.12615 |