Titlebook: NASA Formal Methods; 11th International S Julia M. Badger,Kristin Yvonne Rozier Conference proceedings 2019 Springer Nature Switzerland AG

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https://doi.org/10.1007/978-3-030-20652-9formal methods; model checking and theorem proving; automated static analysis; logic and verification; m

生來

Learning-Based Testing of an Industrial Measurement Device,specially deterministic systems, like Mealy machines, a variety of learning algorithm implementations is readily available. In this paper, we apply this technique to a measurement device from the automotive industry in order to systematically test its behaviour. However, our system under learning sh

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: A Distributed Real-Time Modal Logic,asically be located at several computers spread over a communication network. Extensions of Timed Modal Logics (.) such as, Timed Propositional Modal Logic (.), Timed Modal .-calculus and . have been proposed to capture timed and temporal properties in real-time systems. However, these logics rely o

附錄

Local Reasoning for Parameterized First Order Protocols,rameterized distributed systems. However, specifying many natural objects, such as a ring topology, in FOL is unexpectedly inconvenient. We present a framework based on FOL for specifying distributed multi-process protocols in a process-local manner together with an implicit network topology. In the

FILLY

Generation of Signals Under Temporal Constraints for CPS Testing,o difficult to treat symbolically, meaning that the only reasonable option is to sample a finite number of input signals and simulate the corresponding system behaviours. It is important to choose a sample so that it best “covers” the whole input signal space. We use timed automata to model temporal

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Fissure

Using Standard Typing Algorithms Incrementally,er, the ever-growing size of programs and their continuous evolution require building fast and efficient analysers. A promising solution is ., aiming at only re-typing the ., i.e. those parts of the program that change or are inserted, rather than the entire codebase. We propose an algorithmic schem

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水槽

Automated Backend Selection for , Using Deep Learning,eature different backends and configuration options. Selecting an appropriate configuration for a successful employment becomes increasingly hard. In this article, we use machine learning methods to automate the backend selection for the . model checker. In particular, we explore different approache