Inferring Compact Models of Communication Protocol Entities

Formal Methods for Eternal Networked Software Systems

2011

121

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 exploration phases so-called membership queries are used to construct hypothesis models of a system under learning. In testing phases so-called equivalence queries are used to compare respective hypothesis models to the actual system. These two phases are iterated until a valid model of the target system is produced. We will step-wisely elaborate on this simple algorithmic pattern, its underlying correctness arguments, its limitations, and, in particular, ways to overcome apparent hurdles for practical application. This should provide students and outsiders of the field with an intuitive account of the high potential of this challenging research area in particular concerning the control and validation of evolving reactive systems. MotivationInteroperability remains a fundamental challenge when connecting heterogeneous systems [10]. The Connect Integrated Project [35] aims at overcoming the interoperability barrier by synthesizing required Connectors on the fly in five steps [5,21]: (i) extracting knowledge from, (ii) learning about and (iii) reasoning about the interaction behavior of networked systems, as a basis for (iv) synthesizing Connectors [33,34], and subsequently (v) generating and deploying them for immediate use [9].This chapter focuses on the foundations for step (ii), namely on techniques for leveraging and enriching the extracted knowledge by means of experimentation with the targeted components. Central are here advanced techniques for active automata learning [3,38,4,31,50], which are designed for optimally aggregating, and where necessary completing, the observed behavior.Characteristic for active learning automata learning is its iterative alternation between a "testing" phase for completing the transitions relation of the model aggregated from the observed behavior, and an equivalence checking phase, which This work is supported by the European FP 7 project CONNECT (IST 231167).

Section: Bibliographic Notes and Further Readingmentioning

confidence: 99%

Introduction to Active Automata Learning from a Practical Perspective

Steffen

Formal Methods for Eternal Networked Software Systems

2011

121

“…In recent years, automata learning has been employed to create formal models of real-life systems, such as electronic passports [1], telephony systems [5,7], web applications [14,15], communication protocol entities [3], and malicious networked agents [4]. The wide scope of application areas gives testimony on the universality of the automata learning approach.…”

Section: Introductionmentioning

confidence: 99%

Automated Learning Setups in Automata Learning

Isberner

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

et al. 2012

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 typically are created manually for every single system to be learned. This, however, can be a very time-consuming and thus expensive task, making it desirable to find general solutions that can be reused. This paper discusses how test drivers can be created for LearnLib, a flexible automata learning framework. Starting with the construction of application-specific test drivers by hand, we will discuss how a generic test driver can be employed by means of configuration. This configuration is created manually or (semi-)automatically by analysis of the target system's interface.

“…This has been illustrated in a number of case studies like e.g. the above mentioned concrete setting of Computer Telephony Integrated (CTI) systems [17,16], for Web Services [22], protocol specifications [23] or communication protocol entities [7].…”

Section: Introductionmentioning

confidence: 99%

Automata Learning with Automated Alphabet Abstraction Refinement

Steffen

Lecture Notes in Computer Science

2011

Abstraction is the key when learning behavioral models of realistic systems, but also the cause of a major problem: the introduction of non-determinism. In this paper, we introduce a method for refining a given abstraction to automatically regain a deterministic behavior onthe-fly during the learning process. Thus the control over abstraction becomes part of the learning process, with the effect that detected nondeterminism does not lead to failure, but to a dynamic alphabet abstraction refinement. Like automata learning itself, this method in general is neither sound nor complete, but it also enjoys similar convergence properties even for infinite systems as long as the concrete system itself behaves deterministically, as illustrated along a concrete example.