AdaStop: Cost-Aware Early Stopping for DNN Test Selection

arXiv:2607.05461v1 Announce Type: cross 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 extit{AdaStop}, a framework that estimates the marginal fault discovery rate during testing and stops labeling when the estimated rate falls below the threshold au = 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.

Source: arXiv cs.AI | 2026-07-08