AAAI Conference 2026 Conference Paper
FIXME: Towards End-to-End Benchmarking of LLM-Aided Design Verification
- Gwok-Waa Wan
- SamZaak Wong
- Shengchu Su
- Chenxu Niu
- Ning Wang
- Xinlai Wan
- Qixiang Chen
- Mengnv Xing
We introduce FIXME, the first end-to-end and large-scale benchmark for evaluating Large Language Models (LLMs) in hardware design functional verification (FV). Comprising 747 tasks derived from real-world hardware designs, FIXME spans five core FV sub-sets: specification comprehension, reference model generation, testbench generation, assertion design, and RTL debugging. To ensure high data quality, we developed an AI-human collaborative framework for agile data curation and annotation. This process resulted in 25,000 lines of verified RTL, 35,000 lines of enhanced testbenches, and over 1,200 SystemVerilog Assertions. Furthermore, through expert-guided optimization within the multi-agent aided flow, we achieved a remarkable 45.57% improvement in average functional coverage, underscoring the benchmark's robustness. Through evaluation of state-of-the-art LLMs like GPT-4.1, FIXME identifies key limitations and provides actionable insights, advancing the potential of LLM-driven automation in hardware design functional verification.