Correlations between deep neural network model coverage criteria and model quality

Inspired by the great success of using code coverage as guidance in software testing, a lot of neural network coverage criteria have been proposed to guide testing of neural network models (e.g., model accuracy under adversarial attacks). However, while the monotonic relation between code coverage and software quality has been supported by many seminal studies in software engineering, it remains largely unclear whether similar monotonicity exists between neural network model coverage and model quality. This paper sets out to answer this question. Specifically, this paper studies the correlation between DNN model quality and coverage criteria, effects of coverage guided adversarial example generation compared with gradient decent based methods, effectiveness of coverage based retraining compared with existing adversarial training, and the internal relationships among coverage criteria.

• Software and its engineering → Software testing and debugging; • Computing methodologies → Neural networks.