Analysis and testing of black‐box component‐based systems by inferring partial models

Shahbaz, Muzammil; Groz, Roland

doi:10.1002/stvr.1491

Cited by 31 publications

(7 citation statements)

References 48 publications

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“…In fact, given an input alphabet I of a component, we were implicitly inferring an I-quotient of each component in isolation without defining the Σ-quotient. Moreover, the previous approaches including [39] focused on learning components separately, one component at a time; whereas in this paper, we proposed to test the integrated system avoiding thus unnecessary testing efforts to infer the behaviour which may not be related to the composition.…”

Section: Related Workmentioning

confidence: 97%

Integration testing of communicating systems with unknown components

Groz

Li²,

Petrenko

2014

Ann. Telecommun.

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The verification of a modular system composed of communicating components is a difficult problem, especially when the formal specifications, i.e. models of the components are not available. Conventional testing techniques are not efficient in detecting erroneous interactions of components because interleavings of internal events are difficult to reproduce in a modular asynchronous system. The problem of detecting intermittent errors and other compositional flaws in the absence of components' models is addressed in this paper. A method for inferring a controllable approximation of communicating components through testing is elaborated. The inferred finite state models of components are used to detect compositional problems in the system through reachability analysis. To confirm a flaw in a particular component, a witness trace is used to construct a test applied to the component in isolation. The models are refined at each analysis step thus making the approach iterative. The proposed approach employs techniques for machine inference, testing and reachability analysis, and can reveal flaws due to the nondeterministic behaviour of a modular distributed system, such as unspecified receptions, livelocks, divergences, and races that would not be detected by testing alone. One key advantage of the approach is that by means of the inference parameter, it allows one to find a compromise between the co mplexity of testing the integrated system and the precision of the resulting models. The use of large tests is completely avoided in testing a component in isolation, since only single diagnostic test is executed in each iteration. Another advantage of the approach is that inferred models capture the functionalities of components used in the given system; unused behaviours of components are not modelled.

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Section: Related Workmentioning

confidence: 97%

Integration testing of communicating systems with unknown components

Groz

Li²,

Petrenko

2014

Ann. Telecommun.

Self Cite

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“…The models were used as a basis for testing the system. In recent years, generating models for testing has been continued by different authors for several types of systems, e.g., for Event-B models [23,24], for graphical user interfaces of android applications [18,19], and for integration testing [64] of automotive components. Meinke and Sindhu present LBTest, a tool for learning-based testing for reactive systems, integrating model checking, active automata learning, and random testing [55].…”

Section: Related Work and Applicationsmentioning

confidence: 99%

Combining Black-Box and White-Box Techniques for Learning Register Automata

Howar

Jönsson

Vaandrager

2019

Lecture Notes in Computer Science

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Model learning is a black-box technique for constructing state machine models of software and hardware components, which has been successfully used in areas such as telecommunication, banking cards, network protocols, and control software. The underlying theoretic framework (active automata learning) was first introduced in a landmark paper by Dana Angluin in 1987 for finite state machines. In order to make model learning more widely applicable, it must be further developed to scale better to large models and to generate richer classes of models. Recently, various techniques have been employed to extend automata learning to extended automata models, which combine control flow with guards and assignments to data variables. Such techniques infer guards over data parameters and assignments from observations of test output. In the black-box model of active automata learning this can be costly and require many tests, while in many application scenarios source code is available for analysis. In this paper, we explore some directions for future research on how black-box model learning can be enhanced using white-box information extraction methods, with the aim to maintain the benefits of dynamic black-box methods while making effective use of information that can be obtained through white-box techniques.

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“…There have been industrial applications, for instance, on regression testing of telecommunication systems at Siemens, 20 on integration testing at France Telecom, 36 on automatic testing of an online conference service of Springer Verlag, 39 and on testing requirements of a brake-by-wire system from Volvo Technology. 16 Below, I review some representative case studies that have been carried out at Radboud University related to smart cards, network protocols, and legacy software.…”

Section: Examples Of Applicationsmentioning

confidence: 99%

Magnifying motions the right way

Szeliski

2016

Commun. ACM

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Analysis and testing of black‐box component‐based systems by inferring partial models

Cited by 31 publications

References 48 publications

Integration testing of communicating systems with unknown components

Integration testing of communicating systems with unknown components

Combining Black-Box and White-Box Techniques for Learning Register Automata

Magnifying motions the right way

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