Boundary sampling to boost mutation testing for deep learning models

Context: The prevalent application of Deep Learning (DL) models has raised concerns about their reliability. Due to the data-driven programming paradigm, the quality of test datasets is extremely important to gain accurate assessment of DL models. Recently, researchers have introduced mutation testing into DL testing, which applies mutation operators to generate mutants from DL models, and observes whether the test data can identify mutants to check the quality of test dataset. However, there still exist many factors (e.g., huge labeling efforts and high running cost) hindering the implementation of mutation testing for DL models.