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The concurrency workbench: a semantics-based tool for the verification of concurrent systems
TLDR
The Concurrency Workbench is an automated tool for analyzing networks of finite-state processes expressed in Milner's Calculus of Communicating Systems and a large number of interesting verification methods can be formulated as combinations of a small number of primitive algorithms.
Reactive, Generative and Stratified Models of Probabilistic Processes
TLDR
Three models of probabilistic processes, namely, reactive, generative, and stratified, are investigated within the context of PCCS, an extension of Milner′s SCCS in which each summand of a process summation expression is guarded by a probability and the sum of these probabilities is 1.
Optimal code motion: theory and practice
An implementation-oriented algorithm for lazy code motion is presented that minimizes the number of computations in programs while suppressing any unnecessary code motion in order to avoid
LearnLib: a framework for extrapolating behavioral models
TLDR
The LearnLib is presented, a library of tools for automata learning explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding optimizations, and its modular structure allows users to configure their own tailored learning scenarios.
Reactive, generative, and stratified models of probabilistic processes
TLDR
A structural operational semantics of PCCS is given as a set of inference rules for each of the models, a notion of bisimulation semantics, and some conference proofs are presented.
Lazy code motion
TLDR
The point of the bit-vector algorithm is the decomposition of the bi-directional structure of the known placement algorithms into a sequence of a backward and a forward analysis, which directly implies the efficiency result.
The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning
TLDR
The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity, thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information.
LearnLib: a library for automata learning and experimentation
TLDR
The LearnLib is presented, a library for automata learning and experimentation that allows users to configure their tailored learning scenarios, which exploit specific properties of the envisioned applications.
The Open-Source LearnLib - A Framework for Active Automata Learning
TLDR
The current, open-source version of LearnLib was completely rewritten from scratch, incorporating the lessons learned from the decade-spanning development process of the previous versions oflearnLib.
Introduction to Active Automata Learning from a Practical Perspective
In this chapter we give an introduction to active learning of Mealy machines, an automata model particularly suited for modeling the behavior of realistic reactive systems. Active learning is
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