An open framework for automata learning
On this page you can find papers that are related to LearnLib, our related projects or automata learning in general. If you are interested in papers that are citing LearnLib, have a look at Google Scholar.
In this paper we present TTT, a novel active automata learning algorithm formulated in the Minimally Adequate Teacher (MAT) framework. The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity. [...] Read full abstract »
In the past decade, active automata learning, an originally merely theoretical enterprise, got attention as a method for dealing with black-box or third party systems. Applications ranged from the support of formal verification, e.g. for assume guarantee reasoning [4], to usage of learned models [...] Read full abstract »
Test drivers are an essential part of any practical active automata learning setup. These components to accomplish the translation of abstract learning queries into concrete system invocations while managing runtime data values in the process. In current practice test drivers typical [...] Read full abstract »
We will demonstrate the impact of the integration of our most recently developed learning technology for inferring Register Automata into the LearnLib, our framework for active automata learning. This will not only illustrate the unique power of Register Automata, which allows one to faithfully m [...] Read full abstract »
The Next Generation LearnLib (NGLL) is a framework for model-based construction of dedicated learning solutions on the basis of extensible component libraries, which comprise various methods and tools to deal with realistic systems including test harnesses, reset mechanisms and abstraction/refine [...] Read full abstract »
In this paper, we show how to fully automatically infer semantic interfaces of data structures on the basis of systematic testing. Our semantic interfaces are a generalized form of Register Automata (RA), comprising parameterized input and output, allowing t [...] Read full abstract »
This paper reviews the development of active learning in the last decade under the perspective of treating of data, a major source of undecidability, and therefore a key problem to achieve practicality. Starting with the first case studies, in which data was completely disregarded, we revisit dif [...] Read full abstract »
In this paper, we present an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow. Register automata operate on an infinite data domain, whose values can be assigne [...] Read full abstract »
We present an active automata learning algorithm for Mealy state machines that directly constructs a state machine hypothesis according to observations, while other algorithms generate a state machine as output from information gathered in an observation table. Our DHC algorithm star [...] Read full abstract »
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 characterized by its alternation of an exploration phase and a testing phase. During explora [...] Read full abstract »