ATLTest: A White-Box Test Generation Approach for ATL Transformations

González, Carlos A.; Cabot, Jordi

doi:10.1007/978-3-642-33666-9_29

Cited by 33 publications

(32 citation statements)

References 18 publications

(18 reference statements)

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“…This technique enabled testing the execution of the implemented transformation, so the obtained result could be evaluated and compared to the expected result. The generation of test models by means of black-box technique has become more popular since they do not need to deal with the transformation language or technology (González and Cabot, 2012). …”

Section: Discussionmentioning

confidence: 99%

“…Compared to software testing, model transformation testing bears an additional challenge: The complex nature of model transformation (González and Cabot, 2012). Generally, models are formed by elements of different kind and must conform to a meta-model, what hampers the generation of test case models and the analysis of the final result obtained.…”

Section: Discussionmentioning

confidence: 99%

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Applying Black-Box Testing to Model Transformations in the Model Driven Architecture Context

Agner¹,

Soares²,

Simão³

et al. 2014

Journal of Computer Science

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Testing model transformations has played a leading role with the dissemination of MDA in software development processes. Software testing based on black-box testing, together with the "category partitioning" method, can be efficiently used in order to conduct the verification of model transformations. This study employs software testing techniques to an ATL model transformation in the MDA context and points out their benefits. The black-box testing method was adapted to the MT-PROAPES model transformation based on profiles and platform models. The platform models define the range of input models of the MT-PROAPES and are used for the creation of the test cases. The test cases were selected so as to meet certain requirements and increase the ability to detect errors in the model transformation. This approach makes the test process more agile and does not require any abstraction of behavioral properties of the transformations. The field of transformation testing and verification still faces significant challenges and requires a lot of research. Although having some limitations, black-box testing conforms to various situations, besides allowing its integration with other test strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Applying Black-Box Testing to Model Transformations in the Model Driven Architecture Context

Agner¹,

Soares²,

Simão³

et al. 2014

Journal of Computer Science

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“…In particular, given a set of input models generated from a specification, we plan to extend them to create new models that enable the testing of a transformation implementation according to some white-box coverage criterion (e.g. rule coverage, OCL expression coverage [21], etc.). The implementation of a mechanism for the detection and elimination of redundant models in the generated test sets is also future work.…”

Section: Discussion and Lines Of Future Workmentioning

confidence: 99%

“…For example, in [33], the authors propose using all possible overlapping models of each pair of rules in a transformation as input models for testing. More recently, [21] exploits the structure of ATL transformations to generate input models: they extract the dependency graph of the transformations (similar to a control flow graph), and each traversal of the graph is transformed into a set of constraints used to compute an input model by means of constraint solving. Coverage criteria.…”

Section: State Of the Artmentioning

confidence: 99%

“…A drawback in these works is that some transformation properties of interest may remain untested as they are not taken into account when building (manually or automatically) test models. On their turn, white-box testing approaches usually adopt a mix of classical white-box coverage criteria [21] and others specific for transformations, such as rule coverage or decision coverage [41]. For instance, in [41], the authors measure the decision coverage of input test sets, however, there is no discussion of the correlation between this coverage criterion and the efficacy of a test set.…”

Section: State Of the Artmentioning

confidence: 99%

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Specification-driven model transformation testing

Guerra

Soeken

2013

Softw Syst Model

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Testing model transformations poses several challenges, among them the automatic generation of appropriate input test models and the specification of oracle functions. Most approaches to the generation of input models ensure a certain coverage of the source meta-model or the transformation implementation code, whereas oracle functions are frequently defined using query or graph languages. However, these two tasks are usually performed independently regardless of their common purpose, and sometimes there is a gap between the properties exhibited by the generated input models and those considered by the transformations.Recently, we proposed a formal specification language for the declarative formulation of transformation properties (by means of invariants, pre-and postconditions) from which we generated partial oracle functions used for transformation testing. Here, we extend the usage of our specification language for the automated generation of input test models by SAT solving. The testing process becomes more intentional because the generated models ensure a certain coverage of the transformation requirements. Moreover, we use the same specification to consistently derive both the input test models and the oracle functions. A set of experiments is presented, aimed at measuring the efficacy of our technique.

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Model transformation testing: a bi-level search-based software engineering approach

Sahin

Kessentini

Wimmer

et al. 2015

J. Softw. Evol. and Proc.

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The process of writing model transformations is a complex and error-prone one. Thus, efficient techniques and tools for validating model transformations are needed. One of them is model transformation testing. The generation of test cases for model transformations is mainly based on metamodel and rules coverage criteria. In this paper, we propose to treat model transformation testing as a bi-level optimization problem to combine the generation of test cases with mutation testing. In our adaptation, the upper-level problem generates a set of test cases that maximizes the coverage of metamodels and errors introduced by the lower level to the transformation rules. The lower level maximizes the number of generated errors in the rules that cannot be detected by the test cases produced by the upper level. The main advantage of our bi-level formulation is that the evaluation of test cases is not limited to the coverage of metamodels, but it allows evaluating their ability to detect errors. The statistical analysis of our experiments on different transformation mechanisms confirms the outperformance of our bi-level proposal compared with state-of-the-art model transformation testing techniques. Figure 8. Precision median values of bi-level optimization problem (BLOP), co-evolutionary algorithm (Co-Evol), and Fleurey et al. [7] over 31 independent simulation runs. CLD, class diagram; RDBMS, relational databases. 832 DILAN SAHIN ET AL.

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ATLTest: A White-Box Test Generation Approach for ATL Transformations

Cited by 33 publications

References 18 publications

Applying Black-Box Testing to Model Transformations in the Model Driven Architecture Context

Applying Black-Box Testing to Model Transformations in the Model Driven Architecture Context

Specification-driven model transformation testing

Model transformation testing: a bi-level search-based software engineering approach

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