Neuron importance-aware coverage analysis for deep neural network testing

Abstract

Deep Neural Network (DNN) models are widely used in many cutting-edge domains, such as medical diagnostics and autonomous driving. However, an urgent need to test DNN models thoroughly has increasingly risen. Recent research proposes various structural and non-structural coverage criteria to measure test adequacy. Structural coverage criteria quantify the degree to which the internal elements of DNN models are covered by a test suite. However, they convey little information about individual inputs and exhibit limited correlation with defect detection. Additionally, existing non-structural coverage criteria are unaware of neurons’ importance to decision-making. This paper addresses these limitations by proposing novel non-structural coverage criteria. By tracing neurons’ cumulative contribution to the final decision on the training set, this paper identifies important neurons of DNN models. A novel metric is proposed to quantify the difference in important neuron behavior between a test input and the training set, which provides a measured way at individual test input granularity. Additionally, two non-structural coverage criteria are introduced that allow for the quantification of test adequacy by examining differences in important neuron behavior between the testing and the training set. The empirical evaluation of image datasets demonstrates that the proposed metric outperforms the existing non-structural adequacy metrics by up to 14.7% accuracy improvement in capturing error-revealing test inputs. Compared with state-of-the-art coverage criteria, the proposed coverage criteria are more sensitive to errors, including natural errors and adversarial examples.