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

Li, Keqin; Groz, Roland; Shahbaz, Muzammil

doi:10.1007/11888116_31

Cited by 19 publications

(27 citation statements)

References 5 publications

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“…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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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.

View full text Add to dashboard Cite

show abstract

“…There are other similar works, e.g., [14] and [19] that propose techniques to extract more in-depth knowledge and considerably highlevel state based models from a component, but they are mostly relying on source code. On the contrary, we have proposed a simple parameterized model [13] and an algorithm to infer it from a black box component. In this paper, we extend our work to enrich this model with the notion of predicates and observable nondeterminism.…”

Section: Learning Models In Practicementioning

confidence: 99%

“…Here we adapt the approaches described in [12] and [13] for PFSM. The components we deal with are viewed as black boxes with known input alphabets.…”

Section: Integration Testing Approachmentioning

confidence: 99%

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Learning Parameterized State Machine Model for Integration Testing

Shahbaz

Groz

2007

31st Annual International Computer Software and Applications Conference - Vol. 2 - (COMPSAC 2007)

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Although many of the software engineering activities can now be model-supported, the model is often missing in software development. We are interested in retrieving statemachine models from black-box software components. We assume that the details of the development process of such components (third-party software or COTS) are not available. To adequately support software engineering activities, we need to learn more complex models than simple automata.Our model is an extension of finite state machines that incorporates the notions of predicates and parameters on transitions. We argue that such a model can offer a suitable trade-off between expressivity of the model and complexity of model learning. We have been able to extend polynomial learning algorithms to extract such models in an incremental testing approach. In turn, the models can be used to derive tests or for component documentation.

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“…This subset can be extended in response to new information obtained in counterexamples. Groz, Li, and Shahbaz [24,31,17] extend regular inference to Mealy machines with data values, for use in integration testing. In their work, they select a finite set of representative data values to be supplied together with the input to ta component.…”

Section: Introductionmentioning

confidence: 99%

Learning of Automata Models Extended with Data

Jönsson

2011

Formal Methods for Eternal Networked Software Systems

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Abstract. One of the challenges in the Connect project is to develop techniques for learning models of networked components from exploratory interaction with the component, based on analyzing messages exchanged between the component and its environment. Many approaches to this problem employ regular inference (aka. automata learning) techniques which generate modest-size finite-state models. Most communication with real-life systems involves data values being relevant to the communication context and thus influencing the observable behavior of the communication endpoints. When applying methods from the realm of automata learning, it is desirable to handle such dataoccurrences. It is therefore important to extend inference techniques to handle message alphabets and state-spaces with structures containing data parameters, often with large domains. After very briefly mentioning several approaches to the problem, we give a longer account of an approach proposed by Aarts et al, which adapts ideas from of predicate abstraction, successfully used in formal verification. We illustrate the techniques by application to a simple running example, which models a simple booking service.Acknowledgment This paper builds on joint work with several present and former collaborators, including Fides Aarts,

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

Cited by 19 publications

References 5 publications

Improving dynamic software analysis by applying grammar inference principles

Improving dynamic software analysis by applying grammar inference principles

Learning Parameterized State Machine Model for Integration Testing

Learning of Automata Models Extended with Data

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