Forensic Debugging of Model Transformations

Hibberd, Mark; Lawley, Michael; Raymond, Kerry

doi:10.1007/978-3-540-75209-7_40

Cited by 21 publications

(15 citation statements)

References 21 publications

(20 reference statements)

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“…Regarding dynamic approaches, for which the model transformation execution is needed and therefore input models, the authors in [35,60] present their contribution for debugging model transformations. Also, the work in [4] analyze the execution traces between the source and target models in order to find errors, and in [29] a white-box test model generation approach for testing the transformations is proposed.…”

Section: Testing and Verifying Model Transformationsmentioning

confidence: 99%

Testing models and model transformations using classifying terms

et al. 2016

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This paper proposes the use of equivalence partitioning techniques for testing models and model transformations. In particular, we introduce the concept of classifying terms, which are general OCL terms on a class model enriched with OCL constraints. Classifying terms permit defining equivalence classes, in particular for partitioning the source and target model spaces of the transformation, defining for each class a set of equivalent models with regard to the transformation. Using these classes, a model validator tool is able to automatically construct object models for each class, which constitute relevant test cases for the transformation. We show how this approach of guiding the construction of test cases in an orderly, systematic and efficient manner can be effectively used in combination with Tracts for testing both directional and bidirectional model transformations and for analyzing their behavior.

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Section: Testing and Verifying Model Transformationsmentioning

confidence: 99%

Testing models and model transformations using classifying terms

et al. 2016

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“…Writing transformations with Prolog when tuples are used becomes a very easy task. In [28], they argue that declarative approaches concentrate on what relationships exist between the source and target, compared with imperative approaches which concentrate on how to explicitly transform from the source to the target. Complete and correct transformations can be more probably obtained from declarative transformations which do not have to take into account execution order, source traversal and target creation.…”

Section: Model Transformation and Prologmentioning

confidence: 99%

“…Debugging with breakpoints, step-by-step transformation execution, and variable values is not enough because the first think one wants to know is which rule is not working, why and where. Debugging support has been included in some tools: ATL, GReAT, VIATRA, FUJABA, Tefkat but in [28] they identify several debugging questions to be answered, according to three groups: logical bugs (violation of a constraint between the source and target models), well-formedness bugs (violation of a constraint of the target models) and bug smells (relationship between source, target and transformation). They also establish 4 groups for tracing: tracing from a target object to its contributors, tracing from source objects to target objects, source objects that contributed to the creation of target objects; and source objects that did not contribute to the creation of a target object.…”

Section: Model Transformation and Prologmentioning

confidence: 99%

PTL: A model transformation language based on logic programming

Almendros-Jiménez¹,

Iribarne²,

López-Fernández³

et al. 2016

Journal of Logical and Algebraic Methods in Programming

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In this paper we present a model transformation language based on logic programming. The language, called PTL (Prolog based Transformation Language), can be considered as a hybrid language in which ATL (Atlas Transformation Language)-style rules are combined with logic rules for defining transformations. ATL-style rules are used to define mappings from source models to target models while logic rules are used as helpers. The implementation of PTL is based on the encoding of the ATL-style rules by Prolog rules. Thus, PTL makes use of Prolog as a transformation engine. We have provided a declarative semantics to PTL and proved the semantics equivalent to the encoded program. We have studied an encoding of OCL (Object Constraint Language) with Prolog goals in order to map ATL to PTL. Thus a subset of PTL can be considered equivalent to a subset of ATL. The proposed language can be also used for model validation, that is, for checking constraints on models and transformations. We have equipped our language with debugging and tracing capabilities which help developers to detect programming errors in PTL rules. Additionally, we have developed an Eclipse plugin for editing PTL programs, as well as for debugging, tracing and validation. Finally, we have evaluated the language with several transformation examples as well as tested the performance with large models.

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“…Regarding dynamic approaches, for which the model transformation execution is needed and therefore input models, the authors in [21] and [22] present their contribution for debugging model transformations. Also, the work in [23] analyse the execution traces between the source and target models in order to find errors, and in [24] a white-box test model generation approach for testing the transformations is proposed.…”

Section: B Testing and Verifying Model Transformationsmentioning

confidence: 99%

Employing classifying terms for testing model transformations

Gogolla

Vallecillo

Burgueño

et al. 2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Abstract-This contribution proposes a new technique for developing test cases for UML and OCL models. The technique is based on an approach that automatically constructs object models for class models enriched by OCL constraints. By guiding the construction process through so-called classifying terms, the built test cases in form of object models are classified into equivalence classes. A classifying term can be an arbitrary OCL term on the class model that calculates for an object model a characteristic value. From each equivalence class of object models with identical characteristic values one representative is chosen. The constructed test cases behave significantly different with regard to the selected classifying term. By building few diverse object models, properties of the UML and OCL model can be explored effectively. The technique is applied for automatically constructing relevant source model test cases for model transformations between a source and target metamodel.

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Forensic Debugging of Model Transformations

Cited by 21 publications

References 21 publications

Testing models and model transformations using classifying terms

Testing models and model transformations using classifying terms

PTL: A model transformation language based on logic programming

Employing classifying terms for testing model transformations

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