DeepTest Tool Competition 2026: Benchmarking an LLM-Based Automotive Assistant
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 12 references that are in the map. First posted on arXiv 14 Apr 2026.
Builds on
3 of 12 references.
References · 3
- 2024A Survey on LLM-as-a-Judgecited by 1,423
- 2023Testing of Deep Reinforcement Learning Agents with Surrogate Modelscited by 13
- 2026STELLAR: A Search-Based Testing Framework for Large Language Model Applicationscited by 5
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Citing papers per year: 2026: 1.
1 paper.