Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild
Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their critical role in machine learning infrastructure, their operational challenges and engineering concerns have received limited attention so far. We present an empirical study of 57 evaluation harnesses, deriving a five-stage harness model and classifying 16,560 issues by workflow stage and root cause. Most harness operational challenges concentrate in the Specification stage (41.4% of issues), where harnesses integrate external models, datasets, and scoring judges. The three most frequent root causes of operational c...
| Authors | Zhimin Zhao, Zehao Wang, Abdul Ali Bangash et al. |
|---|---|
| Primary category | cs.SE |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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