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

ALOFT

有节制

What’s to Come is Still Unsureate winning condition permits to specify desirable behavior. The classical model here is the positional game, where both players can (fully or partially) observe the current position in the game graph, which in turn is indicative of their mutual current states. In practice, neither sensing or actuat

emission

prodrome

Robustness Testing of Intermediate Verifierse changes considerably with details of how the input program is expressed—details that should be irrelevant, such as the order of independent declarations. Such a lack of robustness frustrates users who have to spend considerable time figuring out a tool’s idiosyncrasies before they can use it effec

淡紫色花

Simulation Algorithms for Symbolic Automatalabeled by predicates over alphabet symbols. In one approach, we build on the algorithm by Ilie, Navaro, and Yu proposed originally for classical finite automata, modifying it using the so-called mintermisation of the transition predicates. This solution, however, generates all Boolean combinations

档案

Quantitative Projection Coverage for Testing ML-enabled Autonomous Systemsngineered using data-driven approach. We propose quantitative .-projection coverage as a metric to mediate combinatorial explosion while guiding the data sampling process. By assuming that domain experts propose largely independent environment conditions and by associating elements in each condition

Limerick

Recursive Online Enumeration of All Minimal Unsatisfiable Subsetsdesirable to identify the core problems among them. Such cores are called minimal unsatisfiable subsets (MUSes). The more MUSes are identified, the more information about the conflicts among the constraints is obtained. However, a full enumeration of all MUSes is in general intractable due to the la

Vertebra

心胸开阔

组装

Bisimilarity Distances for Approximate Differential Privacyn probability distributions. We study .-differential privacy in the setting of labelled Markov chains. While the exact differences relevant to .-differential privacy are not computable in this framework, we propose a computable bisimilarity distance that yields a sound technique for measuring ., the