AutoSpec: Automated Generation of Neural Network Specifications
<|reference_start|>AutoSpec: Automated Generation of Neural Network Specifications: The increasing adoption of neural networks in learning-augmented systems highlights the importance of model safety and robustness, particularly in safety-critical domains. Despite progress in the formal verification of neural networks, current practices require users to manually define model specifications -- properties that dictate expected model behavior in various scenarios. This manual process, however, is prone to human error, limited in scope, and time-consuming. In this paper, we introduce AutoSpec, the first framework to automatically generate comprehensive and accurate specifications for neural networks in learning-augmented systems. We also propose the first set of metrics for assessing the accuracy and coverage of model specifications, establishing a benchmark for future comparisons. Our evaluation across four distinct applications shows that AutoSpec outperforms human-defined specifications as well as two baseline approaches introduced in this study.<|reference_end|>
@article{jin2024autospec:,
    title={AutoSpec: Automated Generation of Neural Network Specifications},
    author={Shuowei Jin, Francis Y. Yan, Cheng Tan, Anuj Kalia, Xenofon Foukas, Z.
  Morley Mao},
    journal={arXiv preprint arXiv:2409.10897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10897},
    primaryClass={cs.LG cs.SE}
}