Template-based model generation
| Autor: | Minxue Pan, Xiao He, Chang-Jun Hu, Tian Zhang, Zhiyi Ma |
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| Rok vydání: | 2017 |
| Předmět: |
Semantics (computer science)
Computer science 020207 software engineering 02 engineering and technology Folding (DSP implementation) computer.software_genre Metamodeling Set (abstract data type) Digital subscriber line Template Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Template based Data mining computer Software Generator (mathematics) |
| Zdroj: | Software & Systems Modeling. 18:2051-2092 |
| ISSN: | 1619-1374 1619-1366 |
| DOI: | 10.1007/s10270-017-0634-5 |
| Popis: | Given their vital roles in model-based software engineering, the performance of model-related operations (MOs, such as model transformations) must be systematically tested. However, how to produce a set of large input models that conform to structure-related constraints presents a major challenge to such test. This paper proposes a template-based approach to efficient model generation. Firstly, a DSL is provided to describe templates that specify how to generate a valid model that conforms to structure-related constraints. Secondly, a folding semantic is defined to convert templates into a wrapper metamodel. Thirdly, a wrapper model is generated using the existing model generators (e.g., a random model generator) according to the wrapper metamodel. Fourthly, an unfolding semantics is specified to translate the wrapper model into the desired test input. This paper also presents five case studies to evaluate the proposed approach, and the results demonstrate that such approach can generate large models based on structure-related constraints and facilitate the performance testing of MOs. |
| Databáze: | OpenAIRE |
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