A Fine-Grained Evaluation of Mutation Operators for Deep Learning Systems: A Selective Mutation Approach
Yichun Wang, Zhiyi Zhang, Yongming Yao, Zhiqiu Huang · 2023
The widespread adoption of deep learning (DL) has made it critical to ensure its reliability. Mutation testing has been employed in DL testing to assess test data quality, but it can be costly of a large number of generated mutants. Cost reduction can be achieved by selecting a sufficient subset of mutation operators. However, it remains unclear to what extent the DL mutation operators contribute to test effectiveness, making it challenging to determine which are useful mutation operators in a selective mutation approach.