Domain-Specific Optimization in Automata Learning

Hungar, Hardi; Niese, Oliver; Steffen, Bernhard

doi:10.1007/978-3-540-45069-6_31

Cited by 99 publications

(68 citation statements)

References 14 publications

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“…As in previous related work [3,4], we adapt Angluin's algorithm [1] to a testing context, in an incremental approach. Our contribution extends this approach in two directions.…”

Section: Resultsmentioning

confidence: 99%

“…In requirements engineering approaches [7,8], the equivalence query used to get counterexamples is provided by an expert. Here we follow the approach used in [4,3] where the query is implemented by testing the integrated system which acts as an oracle. The outline of the integration methodology is as follows.…”

Section: Our Approachmentioning

confidence: 99%

“…A general approach [4,2,3] is to generate formal models of COTS through their incremental learning. In [6], we proposed an approach to learn I/O models of COTS (using a slight modification of Angluin's Algorithm [1]), and define an Integration Testing Procedure based upon these models.…”

Section: Introductionmentioning

confidence: 99%

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Integration Testing of Distributed Components Based on Learning Parameterized I/O Models

Li¹,

Groz²,

Shahbaz

2006

Lecture Notes in Computer Science

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Abstract. The design of complex systems, e.g., telecom services, is usually based on the integration of components (COTS). When components come from third party sources, their internal structure is usually unknown and the documentation is scant or inadequate.Our work addresses the issue of providing a sound support to component integration in the absence of formal models. We consider components as black boxes and use an incremental learning approach to infer partial models. At the same time, we are focusing on the richer models that are more expressive in the designing of complex systems. Therefore, we propose an I/O parameterized model and an algorithm to infer it from a black box component. This is combined with interoperability testing covering models of the components.

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“…As in previous related work [3,4], we adapt Angluin's algorithm [1] to a testing context, in an incremental approach. Our contribution extends this approach in two directions.…”

Section: Resultsmentioning

confidence: 99%

Section: Our Approachmentioning

confidence: 99%

See 1 more Smart Citation

Integration Testing of Distributed Components Based on Learning Parameterized I/O Models

Li¹,

Groz²,

Shahbaz

2006

Lecture Notes in Computer Science

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show abstract

“…A large number of techniques exist that use the L * algorithm to build models from software systems [28,29]. These are interesting because they are active, and incorporate negative information about system behaviour to produce a complete model of system behaviour.…”

Section: Existing Solutions Inspired By Grammar Inferencementioning

confidence: 99%

Improving dynamic software analysis by applying grammar inference principles

Walkinshaw¹,

Bogdanov²,

Holcombe³

et al. 2008

J. Softw. Maint. Evol.: Res. Pract.

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SUMMARYGrammar inference is a family of machine learning techniques that aim to infer grammars from a sample of sentences in some (unknown) language. Dynamic analysis is a family of techniques in the domain of software engineering that attempts to infer rules that govern the behaviour of software systems from a sample of executions. Despite their disparate domains, both fields have broadly similar aims; they try to infer rules that govern the behaviour of some unknown system from a sample of observations. Deriving general rules about program behaviour from dynamic analysis is difficult because it is virtually impossible to identify and supply a complete sample of necessary program executions. The problems that arise with incomplete input samples have been extensively investigated in the grammar inference community. This has resulted in a number of advances that have produced increasingly sophisticated solutions that are more successful at accurately inferring grammars from (potentially) sparse information about the underlying system. This paper investigates the similarities and shows how many of these advances can be applied with similar effect to dynamic analysis problems by a series of small experiments on random state machines.

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“…The Learnlib toolkit [49], winner of the 2010 Zulu active state machine learning competition [21], can therefore learn models consisting of up to tens of thousands of states. Active state machine learning tools have been used successfully for many different applications in software engineering such as regression testing of software components [40], fuzz testing of protocol implementations [22], and inference of botnet protocols [18].…”

Section: Introductionmentioning

confidence: 99%

Improving active Mealy machine learning for protocol conformance testing

et al. 2013

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Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M R of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to M R . In addition, we also explore an alternative approach in which we learn a model M I of implementation I, which is compared to model M R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning models of and revealing errors in implementations, present the new notion of a conformance oracle, and demonstrate how conformance oracles can be used to speed up conformance checking.

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Domain-Specific Optimization in Automata Learning

Cited by 99 publications

References 14 publications

Integration Testing of Distributed Components Based on Learning Parameterized I/O Models

Integration Testing of Distributed Components Based on Learning Parameterized I/O Models

Improving dynamic software analysis by applying grammar inference principles

Improving active Mealy machine learning for protocol conformance testing

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