Concolic Testing for Deep Neural Networks

Deep neural networks (DNNs) have achieved great success in solving several longstanding tasks with near human-level intelligence, e.g., the ancient game of Go, image classification, and natural language processing. As a result, many potential applications are envisaged. However, major concerns have been raised about the readiness of applying this technique to safety-and security-critical systems, where faulty behaviour carries the risk of endangering human lives or potential damage to business. To address these concerns, similar to product development in avionics and automotive industries, a (safety or security) critical system implemented with DNNs, or comprising DNNs components, needs to be thoroughly tested and certified. The software industry relies on testing as a primary means to provide stakeholders with information about the quality of the software product or service under test [1].