Model-Driven Engineering for Adaptive Software Systems in Dynamic Environments
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| Titel: | Model-Driven Engineering for Adaptive Software Systems in Dynamic Environments |
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| Autoren: | ali fahim |
| Quelle: | Wasit Journal of Computer and Mathematics Science, Vol 4, Iss 1 (2025) |
| Verlagsinformationen: | Wasit University, 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | Driven Engineering (MDE), Runtime Adaptation, Self-Adaptive Systems, MAPE-K Loop, Model Transformation, Domain-Specific Modeling, Evaluation Metrics, Metamodel Evolution, Adaptive Architecture, AI in Software Adaptation, Electronic computers. Computer science, QA75.5-76.95 |
| Beschreibung: | In such an environment, an urgent requirement has been felt for systems that can adapt at runtime to changing situations, as available computing environments are more and more dynamic and complex. The research presented in this paper is about the use of Model Driven Engineering (MDE) for systematic support of managing software adaptability. MDE advocates the use of models as core working entities in software development, allowing you to raise the level of abstraction, transformation and consistency of the adaptive processes. The work presents a full-fledged framework that combines MDE principles together with real-time monitoring, self-configuration strategies, and the MAPE-K feedback loop. By means of theoretical study and cross-domain case comparisons (smart environment, education, transportation, and cloud computing), we show how MDE enables the structural, behavior, and parameter adaptation. The quality of adaptation is evaluated based on a series of metrics, including adaptivity time, model similarity, and transformation delay. The results substantiate the distinctive capability of MDE-based approaches for responsiveness and system correctness in volatile environments and draw attention to important limitations with respect to tool maturity, run-time sync service and scale. The paper concludes by discussing potential future endeavors to drive the field forward, including runtime metamodel evolution, AI-driven model adaptation, and decentralized model-driven infrastructures. The work casts MDE as a suitable and generalizable basis for the development of adaptive systems that exhibit resilience to unanticipated failures, context-awareness, and autonomous adaptation. |
| Publikationsart: | Article |
| ISSN: | 2788-5887 2788-5879 |
| DOI: | 10.31185/wjcms.381 |
| Zugangs-URL: | https://doaj.org/article/656e205a80bf4411a078d3e849f20424 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....93e40b4c927266fb5b7c68e9dacf823e |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | In such an environment, an urgent requirement has been felt for systems that can adapt at runtime to changing situations, as available computing environments are more and more dynamic and complex. The research presented in this paper is about the use of Model Driven Engineering (MDE) for systematic support of managing software adaptability. MDE advocates the use of models as core working entities in software development, allowing you to raise the level of abstraction, transformation and consistency of the adaptive processes. The work presents a full-fledged framework that combines MDE principles together with real-time monitoring, self-configuration strategies, and the MAPE-K feedback loop. By means of theoretical study and cross-domain case comparisons (smart environment, education, transportation, and cloud computing), we show how MDE enables the structural, behavior, and parameter adaptation. The quality of adaptation is evaluated based on a series of metrics, including adaptivity time, model similarity, and transformation delay. The results substantiate the distinctive capability of MDE-based approaches for responsiveness and system correctness in volatile environments and draw attention to important limitations with respect to tool maturity, run-time sync service and scale. The paper concludes by discussing potential future endeavors to drive the field forward, including runtime metamodel evolution, AI-driven model adaptation, and decentralized model-driven infrastructures. The work casts MDE as a suitable and generalizable basis for the development of adaptive systems that exhibit resilience to unanticipated failures, context-awareness, and autonomous adaptation. |
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| ISSN: | 27885887 27885879 |
| DOI: | 10.31185/wjcms.381 |