Titlebook: Automated Technology for Verification and Analysis; 16th International S Shuvendu K. Lahiri,Chao Wang Conference proceedings 2018 Springer

Confirm

abysmal

DeepSafe: A Data-Driven Approach for Assessing Robustness of Neural Networkson are guaranteed to be classified correctly. We also introduce the concept of ., which ensures that the neural network is guaranteed not to misclassify inputs within a region to a specific target (adversarial) label. We evaluate DeepSafe on a neural network implementation of a controller for the ne

孵卵器

Carcinogen

Robustness Testing of Intermediate Verifiersf robustness in the verifier. We implemented our technique in a tool called . ., which operates on programs written in the popular Boogie language for verification—used as intermediate representation in numerous program verifiers. Experiments targeting 135 Boogie programs indicate that brittle behav

血統(tǒng)

修正案

entrance

Recursive Online Enumeration of All Minimal Unsatisfiable Subsetsan be terminated at any time. The key feature of our algorithm is that it minimises the number of satisfiability tests and thus speeds up the computation. The algorithm is applicable to an arbitrary constraint domain. We benchmark our algorithm against the state-of-the-art algorithm Marco on the Boo

outrage

Modular Verification of Concurrent Programs via Sequential Model Checkingss initiates queries to its “environment” (which may contain multiple threads). Those queries are answered by sequential verification, if the environment consists of a single thread, or, otherwise, by applying the same hierarchical algorithm on the environment. Our technique is fully automatic, and

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DAFT