Real-world benchmarks
TechniqueReal-world benchmarks are evaluation methodologies that run actual application workloads or curated datasets drawn from production environments to assess performance and correctness, as opposed to synthetic or directed tests. They are widely used in hardware verification (e.g., RISC-V SoC evaluation) and have become central to recent machine-learning research, where new benchmarks such as ComBench and NTP4VC aim to replace decontextualized or synthetic datasets with execution-verified, repository-level data mined from real open-source projects.
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Real-world benchmarks
Definition
Real-world benchmarks are evaluation methodologies that exercise systems — whether hardware, software, or machine-learning models — using workloads, datasets, or test cases drawn from actual production or open-source environments rather than from purely synthetic or hand-crafted stimuli. They are intended to capture the complexity, diversity, and constraints that systems encounter in deployment, providing a more faithful measure of performance and correctness than idealized test suites.
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