Automata learning is emerging as an effective technique for obtaining state machine models of software and hardware systems. I will present an overview of recent work in which we used active automata learning to find standard violations and security vulnerabilities in implementations of network protocols such as TCP and SSH. Also, I will discuss applications of automata learning to support refactoring of legacy control software and identifying job patterns in manufacturing systems. As a guiding theme in my presentation, I will show how Galois connections (adjunctions) help us to scale the application of learning algorithms to practical problems
In this thesis, we present techniques for more efficient learning and analysis of system behavior. T...
Abstract. History dependent abstraction operators are the key for scal-ing existing methods for acti...
Active automata learning allows to learn software in the form of an automaton representing its behav...
Contains fulltext : 207624.pdf (publisher's version ) (Open Access)ICALP 2019: 46t...
Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer....
Model learning is a black-box technique for constructing state machine models of software and hardwa...
Abstract. We apply automata learning techniques to learn fragments of the TCP network protocol by ob...
In this master thesis we investigate to infer models of standard communication protocols using autom...
Automata Theory is part of computability theory which covers problems in computer systems, software,...
The area of automata learning was pioneered by Angluin in the 80\u27s. Her original algorithm, which...
Being able to model behavior described by a linear sequence of observations (such as log files) goes...
Automata models of learning systems introduced in the 1960s were popularized as learning automata (L...
In the past decade, active automata learning, an originally merely theoretical enterprise, got atten...
Formal models are often used to describe the behavior of a computer program or component. Behavioral...
Automata learning is an established class of techniques for inferring automata models by observing h...
In this thesis, we present techniques for more efficient learning and analysis of system behavior. T...
Abstract. History dependent abstraction operators are the key for scal-ing existing methods for acti...
Active automata learning allows to learn software in the form of an automaton representing its behav...
Contains fulltext : 207624.pdf (publisher's version ) (Open Access)ICALP 2019: 46t...
Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer....
Model learning is a black-box technique for constructing state machine models of software and hardwa...
Abstract. We apply automata learning techniques to learn fragments of the TCP network protocol by ob...
In this master thesis we investigate to infer models of standard communication protocols using autom...
Automata Theory is part of computability theory which covers problems in computer systems, software,...
The area of automata learning was pioneered by Angluin in the 80\u27s. Her original algorithm, which...
Being able to model behavior described by a linear sequence of observations (such as log files) goes...
Automata models of learning systems introduced in the 1960s were popularized as learning automata (L...
In the past decade, active automata learning, an originally merely theoretical enterprise, got atten...
Formal models are often used to describe the behavior of a computer program or component. Behavioral...
Automata learning is an established class of techniques for inferring automata models by observing h...
In this thesis, we present techniques for more efficient learning and analysis of system behavior. T...
Abstract. History dependent abstraction operators are the key for scal-ing existing methods for acti...
Active automata learning allows to learn software in the form of an automaton representing its behav...