DeepTest: automated testing of deep-neural-network-driven autonomous cars
DeepTest: automated testing of deep-neural-network-driven autonomous cars Tian et al., ICSE’18
How do you test a DNN? We’ve seen plenty of examples of adversarial attacks in previous editions of The Morning Paper, but you couldn’t really say that generating adversarial images is enough to give you confidence in the overall behaviour of a model under all operating conditions. Adversarial images approach things from a ‘think like an attacker’ mindset. We want to ‘think like a tester.’ For example, the work on DeepXplore which uses model ensembles to find differences in outputs that suggest bugs. The importance of testing DNNs is especially obvious when it comes to applications such as autonomous driving. Several of the ideas from DeepXplore are used in DeepTest, which looks specifically at testing of autonomous driving system. I think you could apply the DeepTest techniques to test other kinds of DNNs as well.
…despite the tremendous progress, just like traditional software, DNN-based software, including the ones used for autonomous driving, often demonstrate incorrect/unexpected corner-case behaviours that lead to dangerous consequences like a fatal collision.
DeepTest is a system designed to aid in the testing of autonomous driving models. When used to test three of Continue reading

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