TSDTest: A Efficient Coverage Guided Two-Stage Testing for Deep Learning Systems

Abstract

In recent years, Deep Learning systems have been applied to face recognition, autonomous vehicles and other safety-critical fields. Testing Deep Learning systems effectively and adequately is increasingly significant. In this paper, we proposed and implemented TSDTest, a coverage guided two-stage testing framework for deep learning systems. To test more logic for Deep Neuron Network (DNN), TSDTest generates highly diverse test cases with as high neuron coverage as possible during its two stages. Compared with DLFuzz, TSDTest achieved an average 1.75% improvement in neuron coverage and 80.3% more adversarial test inputs on MNIST and Fashion-MNIST. And the step dynamical adjustment also effectively reduces $l_{2}$ distance and avoids the manual identification of test oracle. The implementation of TSDTest shows its effectiveness and superiority in generating diverse test cases and improving the robustness of DNN.