Publications

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.

The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning

Malte Isberner, Falk Howar, Bernhard Steffen: The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning. In RV 2014, LNCS 8734, pp. 307-322. Springer, 2014

Abstract

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. Th [...] Read full abstract »
Download PDF

LearnLib Tutorial: From Finite Automata to Register Interface Programs

Falk Howar, Malte Isberner, Maik Merten, Bernhard Steffen: LearnLib Tutorial: From Finite Automata to Register Interface Programs. In: ISoLA 2012, LNCS 7609, pp. 587-590. Springer, 2012

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 as [...] Read full abstract »
Download PDF

Automated Learning Setups in Automata Learning

Maik Merten, Malte Isberner, Falk Howar, Bernhard Steffen, Tiziana Margaria: Automated Learning Setups in Automata Learning. In: ISoLA 2012, LNCS 7609, pp. 591-607. Springer, 2012

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 »

Download PDF

Demonstrating Learning of Register Automata

Maik Merten, Falk Howar, Bernhard Steffen, Sofia Cassel, Bengt Jonsson: Demonstrating Learning of Register Automata. In: TACAS 2012, LNCS 7214, pp. 466-471. Springer, 2012

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 mode [...] Read full abstract »

Next Generation LearnLib

Maik Merten, Bernhard Steffen, Falk Howar, Tiziana Margaria: Next Generation LearnLib. In: TACAS 2011, LNCS 6605, pp. 220-223. Springer, 2011

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/refinemen [...] Read full abstract »

Inferring Semantic Interfaces of Data Structures

Falk Howar, Malte Isberner, Bernhard Steffen, Oliver Bauer, Bengt Jonsson: Inferring Semantic Interfaces of Data Structures. In: ISoLA 2012, LNCS 7609, pp. 554-571. Springer, 2012

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 to m [...] Read full abstract »
Download PDF

Active Automata Learning: From DFAs to Interface Programs and Beyond

Bernhard Steffen, Falk Howar, Malte Isberner: Active Automata Learning: From DFAs to Interface Programs and Beyond. In: ICGI 2012, JMLR W&CP Vol. 21, pp. 195-209. JMLR, 2012.

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 differ [...] Read full abstract »
Download PDF

Inferring Canonical Register Automata

Falk Howar, Bernhard Steffen, Bengt Jonsson, Sofia Cassel: Inferring Canonical Register Automata. In: VMCAI 2012, LNCS 7148, pp. 251-266. Springer, 2012

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 assigned t [...] Read full abstract »

Automata Learning with on-the-Fly Direct Hypothesis Construction

Maik Merten, Falk Howar, Bernhard Steffen, Tiziana Margaria: Automata Learning with on-the-Fly Direct Hypothesis Construction. In: ISoLA 2011, CCIS 336, pp. 248-260. Springer, 2012

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 »

Download PDF

Introduction to Automata Learning from a Practical Perspective

Bernhard Steffen, Falk Howar, Maik Merten: Introduction to Automata Learning from a Practical Perspective. In: SFM 2011, LNCS 6659, pp. 256-296. Springer, 2011

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 »

Download PDF