Multi-criteria test cases selection for model transformations

Alkhazi, Bader; Abid, Chaima; Kessentini, Marouane; LeRoy, D. J.; Wimmer, Manuel

doi:10.1007/s10515-020-00271-w

Cited by 11 publications

(5 citation statements)

References 84 publications

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“…CPL2SPL, Families2Persons and Grafcet2PetriNet are available on the open-source ATL Zoo respository [5], while SOOML2SOOPL and Families2Persons Extended have been taken from previous research works [69,87]. They all differ in the application domain and have been used as case studies in several papers related to model transformation testing [2,18,22,69,86,87].…”

Section: Case Studiesmentioning

confidence: 99%

“…The concept of structural distance is similar to ours, but the main difference with our approach is that information on structural information needs to be given as input. Finally, Alkhazi et al [2] propose a multiobjective approach for selecting appropriate test cases for the purpose of transformation testing. They apply the NSGA-II algorithm, but the approach is not targeted at building models from scratch.…”

Section: Related Workmentioning

confidence: 99%

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Ant-colony optimization for automating test model generation in model transformation testing

Karimi,

Kolahdouz-Rahimi,

Troya

2024

Journal of Systems and Software

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Section: Case Studiesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ant-colony optimization for automating test model generation in model transformation testing

Karimi,

Kolahdouz-Rahimi,

Troya

2024

Journal of Systems and Software

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“…Hence, the objective is maximizing the number of generated mutants that cannot be detected by the test cases. Finally, Alkhazi et al [2] present the irst approach for test case selection in the context of MTs using multi-objective search. They employ the non-dominated sorting genetic algorithm NSGA-II [193] to ind the best trade-ofs between two conlicting objectives, namely maximizing MT rule coverage and minimizing execution time.…”

Section: White-box Approachesmentioning

confidence: 99%

Model Transformation Testing and Debugging: A Survey

et al. 2022

Self Cite

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Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state of the art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This paper presents a survey on testing and debugging model transformations based on the analysis of 140 papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorise the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.

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“…As a specific characteristic, while this case study uses a large number of classes in a complex inheritance hierarchy along with WF constraints, only few of them can be translated into linear inequalities for scope propagation. In addition to their practical relevance, the these two languages have been used as case studies by multiple model generation papers [32], [36], [50], [51], [52], [53].…”

Section: Domainsmentioning

confidence: 99%

Automated Generation of Consistent Graph Models With Multiplicity Reasoning

Marussy

Semeráth

Varró

2022

IIEEE Trans. Software Eng.

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Advanced tools used in model-based systems engineering (MBSE) frequently represent their models as graphs. In order to test those tools, the automated generation of well-formed (or intentionally malformed) graph models is necessitated which is often carried out by solver-based model generation techniques. In many model generation scenarios, one needs more refined control over the generated unit tests to focus on the more relevant models. Type scopes allow to precisely define the required number of newly generated elements, thus one can avoid the generation of unrealistic and highly symmetric models having only a single type of elements. In this paper, we propose a 3-valued scoped partial modeling formalism, which innovatively extends partial graph models with predicate abstraction and counter abstraction. As a result, well-formedness constraints and multiplicity requirements can be evaluated in an approximated way on incomplete (unfinished) models by using advanced graph query engines with numerical solvers (e.g. IP or LP solvers). Based on the refinement of 3-valued scoped partial models, we propose an efficient model generation algorithm that generates models that are both well-formed and satisfy the scope requirements. We show that the proposed approach scales significantly better than existing SAT-solver techniques or the original graph solver without multiplicity reasoning. We illustrate our approach in a complex design-space exploration case study of collaborating satellites introduced by researchers at NASA Jet Propulsion Lab.

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Multi-criteria test cases selection for model transformations

Cited by 11 publications

References 84 publications

Ant-colony optimization for automating test model generation in model transformation testing

Ant-colony optimization for automating test model generation in model transformation testing

Model Transformation Testing and Debugging: A Survey

Automated Generation of Consistent Graph Models With Multiplicity Reasoning

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