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We present Assume-Guarantee-Repair (AGR)—a novel framework which verifies that a program satisfies a set of properties and also repairs the program in case the verification fails. We consider communicating programs—these are simple C-like programs, extended with synchronous actions over communication channels. Our method, which consists of a learning-based approach to assume–guarantee reasoning, performs verification and repair simultaneously: in every iteration, AGR either makes another step towards proving that the (current) system satisfies the required properties, or alters the system in a way that brings it closer to satisfying the properties. To handle infinite-state systems we build finite abstractions, for which we check the satisfaction of complex properties that contain first-order constraints, using both syntactic and semantic-aware methods. We implemented AGR and evaluated it on various communication protocols. Our experiments present compact proofs of correctness and quick repairs. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage, k-multisection neuron coverage, top-k neuron coverage, neuron boundary coverage, strong neuron activation coverage and modified condition/decision coverage. We evaluate the metrics on realistic DNN models used for perception tasks (LeNet-1, LeNet-4, LeNet-5, ResNet20) including networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, to compare different coverage measures, and to more conveniently inspect the model's internals during testing. [ABSTRACT FROM AUTHOR]