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New benchmark reveals LLM agents struggle with behaviorally adequate test generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Req2Test-Bench, a new benchmark designed to evaluate the ability of large language model agents to generate behaviorally adequate tests from software requirements. The benchmark comprises over 5,000 execution-validated instances from eight open-source projects, pairing natural-language requirements in Gherkin with their corresponding developer-written test implementations. Initial evaluations showed a significant gap between generating tests that execute successfully and those that are behaviorally adequate, with the top-performing configuration achieving only 33.8% behavioral adequacy despite an 82.8% execution success rate. AI

IMPACT Highlights a critical gap in LLM agent capabilities for software testing, suggesting current models are insufficient for generating behaviorally adequate tests without human oversight.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals LLM agents struggle with behaviorally adequate test generation

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The cluster contains a research paper introducing a new benchmark for evaluating LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Binhang Qi, Yiming Liu, Yun Lin, Yangjie Zhou, Hailong Sun, Jin Song Dong ·

    Benchmarking LLM Agents for Behaviorally Adequate Test Generation from Software Requirements

    arXiv:2610.36584v1 Announce Type: cross Abstract: As large language model agents increasingly compile natural-language requirements into repository-level code, requirement-to-test generation grows in importance to provide validation constraints (or harness) to guide code generati…

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