FSM-based conformance testing methods: A survey annotated with experimental evaluation

Dorofeeva, Rita; El-Fakih, Khaled; Maag, Stéphane; Cavalli, Ana; Yevtushenko, Nina

doi:10.1016/j.infsof.2010.07.001

Cited by 132 publications

(86 citation statements)

References 29 publications

(71 reference statements)

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“…4) will have no effect. Detailed comparisons involving HSI and other methods can be found in (Dorofeeva et al 2010;Endo and Simao 2013). Another method which is similar to the method we propose is the SC method (Petrenko and Yevtushenko 2005) (see also Sect.…”

Section: Threats To Validity and Discussionmentioning

confidence: 99%

“…Researchers have investigated means to reduce the test set, while keeping the same properties. Experimental results on test set sizes comparing the existing methods can be found in (Dorofeeva et al 2010;Endo and Simao 2013). These studies show that the recent methods (H and SPY) are able to produce test sets smaller than the traditional ones (W, Wp, and HSI).…”

Section: Introductionmentioning

confidence: 98%

“…6 presents the conclusion and discusses future work. Yannakakis (1996), Lai (2002), Hierons et al (2009), andDorofeeva et al (2010). In this area, the problem of selecting a test set that demonstrates the conformance between a specification and an implementation is arguably pivotal.…”

Section: Introductionmentioning

confidence: 99%

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Fault domain-based testing in imperfect situations: a heuristic approach and case studies

et al. 2014

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Model-based testing (MBT) involves creating an abstraction, called a model, to represent the system and automatically deriving test cases from this model. MBT can be performed using various approaches that generally employ certain assumptions or requirements affecting the test performance in practice. Here, we consider the harmonized state identifiers (HSI) method, which is based on finite state machine (FSM) models and generates test sets that cover all faults in a given domain under certain conditions. We are interested in the application of the HSI method in practical scenarios where some conditions do not hold or are not straightforward to satisfy. Thus, we propose a heuristic extension to the HSI method, called heuristic HSI (HHSI), to consider imperfect situations The authors are alphabetically ordered.Electronic supplementary material The online version of this article (doi:10.1007/s11219-014-9242-6) contains supplementary material, which is available to authorized users.

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Section: Threats To Validity and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Fault domain-based testing in imperfect situations: a heuristic approach and case studies

et al. 2014

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“…However, it is rather difficult to mechanically reproduce the situations corresponding to all exception handling and timeout mechanisms in real environment and test whether the corresponding reactions for all the inputs are carried out correctly. Therefore, a lot of methods for conformance testing of communication protocols modeled as FSMs have been developed, especially in 1980s and 1990s (for survey, see [21,40,45,69]). Those testing methods for FSMs are classified as active testing.…”

Section: Active Testingmentioning

confidence: 99%

A survey on formal active and passive testing with applications to the cloud

2015

Self Cite

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“…In particular, notions similar to the nucleus of an FSM and to distinguishing suffixes of states, which are used to define characteristic samples, are also used in [12,16]. The connection between conformance testing and regular inference is made explicit in [8] and [32] describes how an active learning algorithm can be used for fault detection.…”

Section: Introductionmentioning

confidence: 99%

Learning Moore Machines from Input-Output Traces

Giantamidis

Tripakis

2016

FM 2016: Formal Methods

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Abstract. The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample.

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FSM-based conformance testing methods: A survey annotated with experimental evaluation

Cited by 132 publications

References 29 publications

Fault domain-based testing in imperfect situations: a heuristic approach and case studies

Fault domain-based testing in imperfect situations: a heuristic approach and case studies

A survey on formal active and passive testing with applications to the cloud

Learning Moore Machines from Input-Output Traces

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