Rule-Based Testing of Neural Networks.

Adequate testing of deep neural networks (DNNs) is challenging due to lack of formal requirements and specifications of functionality. In this work, we aim to improve DNN testing by addressing this central challenge. The core idea is to drive testing of DNNs from rules abstracting the network behavior. These rules are automatically extracted from a trained model based on monitoring its neuron values when running on a set of labeled data, and are validated on a separate test set. We show how these rules can be leveraged to improve fundamental testing activities, such as generating test oracles and supporting testing coverage with semantic meaning.