Example-based model-transformation testing

Kessentini, Marouane; Sahraoui, Houari; Boukadoum, Mounir

doi:10.1007/s10515-010-0079-3

Cited by 20 publications

(16 citation statements)

References 30 publications

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“…Except for semantics preservation, there is another viewpoint for the verification of model transformations, such as the work of Guerra et al (2013) and Kessentini et al (2011). They verify some transformation requirements or properties on the execution of actual model transformations written by a dedicated language like ATL, QVT or Kermeta.…”

Section: The Verification Methods Of Model Transformationsmentioning

confidence: 99%

“…The goal of such a translation is to reuse existing verification and analysis tools and their formal model of computation and communication for the purpose of validating the AADL models. One challenge, however, is the problem of proving that the translation itself preserves the intended semantics of the AADL model in the first place or, at least, some of the specific properties or requirements it needs to satisfy (Cabot et al, 2010;Lano and Rahimi, 2013;Giese et al, 2006;Narayanan and Karsai, 2008;Narayanan, 2008;Guerra et al, 2013;Kessentini et al, 2011;Mottu et al, 2008;Xiong et al, 2007). This paper presents a machine checked semantics-preserving transformation of a subset of AADL into Timed Abstract State Machines (TASM) (Ouimet andLundqvist, 2006, 2008).…”

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confidence: 99%

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From AADL to Timed Abstract State Machines: A verified model transformation

Yang¹,

Hu²,

Ma³

et al. 2014

Journal of Systems and Software

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12950 To verify and analyze the AADL models, model transformation technologies are often used to automatically extract a formal specification suitable for analysis and verification. In this process, it remains a challenge to prove that the model transformation preserves the semantics of the initial AADL model or, at least, some of the specific properties or requirements it needs to satisfy. This paper presents a machine checked semantics-preserving transformation of a subset of AADL (including periodic threads, data port communications, mode changes, and the AADL behavior annex) into Timed Abstract State Machines (TASM). The AADL standard itself lacks at present a formal semantics to make this translation validation possible. Our contribution is to bridge this gap by providing two formal semantics for the subset of AADL. The execution semantics provided by the AADL standard is formalized as Timed Transition Systems (TTS). This formalization gives a reference expression of AADL semantics which can be compared with the TASM-based translation (for verification purpose). Finally, the verified transformation is mechanized in the theorem prover Coq.

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Section: The Verification Methods Of Model Transformationsmentioning

confidence: 99%

mentioning

confidence: 99%

From AADL to Timed Abstract State Machines: A verified model transformation

Yang¹,

Hu²,

Ma³

et al. 2014

Journal of Systems and Software

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“…Second, partial oracle functions expressed as contracts have been proposed for checking the validity of input models, output models, and their relationships. In addition, a third approach has recently been proposed [28], to specify oracle functions solely based on the trace links between the input models and output models. In the following, we elaborate on these three kinds, namely verification by model comparison, verification by trace analysis, and verification by contract.…”

Section: Related Workmentioning

confidence: 99%

“…A complementary approach to model transformation testing has been proposed in [28] by using a generic oracle function. The idea of this approach is that the traces between the source and target models of a transformation should be similar to existing example traces.…”

Section: Related Workmentioning

confidence: 99%

Automated verification of model transformations based on visual contracts

et al. 2012

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Model-Driven Engineering promotes the use of models to conduct the different phases of the software development. In this way, models are transformed between different languages and notations until code is generated for the final application. Hence, the construction of correct Model-to-Model (M2M) transformations becomes a crucial aspect in this approach.Even though many languages and tools have been proposed to build and execute M2M transformations, there is scarce support to specify correctness requirements for such transformations in an implementation-independent way, i.e., irrespective of the actual transformation language used.In this paper we fill this gap by proposing a declarative language for the specification of visual contracts, enabling the verification of transformations defined with any transformation language. The verification is performed by compiling the contracts into QVT to detect disconformities of transformation results with respect to the contracts. As a proof of concept, we also report on a graphical modeling environment for the specification of contracts, and on its use for the verification of transformations in several case studies.

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“…Providing an oracle for model transformation testing was discussed by Kessentini et al (2011a). They used "an example" based method to generate oracle functions.…”

Section: Prominent Test Oraclesmentioning

confidence: 99%

Artificial neural networks as multi-networks automated test oracle

et al. 2011

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One of the important issues in software testing is to provide an automated test oracle. Test oracles are reliable sources of how the software under test must operate. In particular, they are used to evaluate the actual results produced by the software. However, in order to generate an automated test oracle, it is necessary to map the input domain to the output domain automatically. In this paper, Multi-Networks Oracles based on Artificial Neural Networks are introduced to handle the mapping automatically. They are an enhanced version of previous ANN-Based Oracles. The proposed model was evaluated by a framework provided by mutation testing and applied to test two industry-sized case studies. In particular, a mutated version of each case study was provided and injected with some faults. Then, a fault-free version of it was developed as a Golden Version to evaluate the capability of the proposed oracle finding the injected faults. Meanwhile, the quality of the proposed oracle is measured by assessing its accuracy, precision, misclassification error and recall. Furthermore, the results of the proposed oracle are compared with former ANN-based Oracles. Accuracy of the proposed oracle was up to 98.93%, and the oracle detected up to 98% of the injected faults. The results of the study show the proposed oracle has better quality and applicability than the previous model.

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Example-based model-transformation testing

Cited by 20 publications

References 30 publications

From AADL to Timed Abstract State Machines: A verified model transformation

From AADL to Timed Abstract State Machines: A verified model transformation

Automated verification of model transformations based on visual contracts

Artificial neural networks as multi-networks automated test oracle

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