[Submitted on 6 Jul 2026]
Title:AdaStop: Cost-Aware Early Stopping for DNN Test Selection
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Abstract:Existing methods for testing deep neural networks (DNNs) primarily prioritize test inputs likely to reveal model faults under a fixed labeling budget. In practice, choosing that budget is difficult: too little testing misses failures, while too much incurs unnecessary labeling costs. This work studies the stopping problem in DNN testing. We formulate testing as a cost--benefit decision process in which labeling an input incurs cost $c$ and discovering a fault yields value $v$. Based on this formulation, we introduce \textit{AdaStop}, a framework that estimates the marginal fault discovery rate during testing and stops labeling when the estimated rate falls below the threshold $\tau = c/v$. Experiments across multiple datasets, architectures, and selection strategies show that $65$--$84\%$ of faults can be discovered using only $9$--$31\%$ of the labeling budget.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.05461 [cs.LG]
(or arXiv:2607.05461v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.05461
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bonan Shen [view email] [v1] Mon, 6 Jul 2026 01:17:18 UTC (74 KB)
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