Identification and Optimisation of Type-Level Model Queries

Ali, Qurat Ul Ain; Kolovos, Dimitrios S.; Barmpis, Konstantinos

doi:10.1109/models-c53483.2021.00121

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…It computes type information of elements in model transformation and also it detects typing errors with respect to source and target metamodel. Static analysis can be used for a number of purposes such as optimisations in [11,17,18].…”

Section: External Threatsmentioning

confidence: 99%

Optimizing the Execution of Model Transformation Chain

Sahay

2024

Preprint

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Model transformation is the key concept in the field of model-driven engineering. To scale up the model transformation tasks in a cloud-based low-code platform, reusing and composing smaller model transformations enables a complex transformation to be executed sequentially and optimally. To achieve reusing and composition of smaller transformations, we need to identify all the model transformation chains possible in the repository/folder to transform the source model to the target model. Then, with the help of static analysis, the total structural features involved in a model transformation are calculated and the chain with optimum quality criteria are considered to be the best chain. In this paper, we propose an approach that takes the benefit of static analysis and automatic program rewriting to optimize the selected chain. The approach will rewrite the transformations to keep only the rules and bindings that generate the intermediate model elements required to generate target model elements in the model transformation chain. The performance is improved by reducing the execution time and intermediate elements generated required to execute the selected transformation chain. This optimizing the chain' elements fasten the execution of the whole model transformation chain. The optimization of the execution of the model transformation chain helps in achieving the scalable solution that manages several workflows originating from heterogeneous model-driven platforms.

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Section: External Threatsmentioning

confidence: 99%

Optimizing the Execution of Model Transformation Chain

Sahay

2024

Preprint

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“…Program rewriting has proven to be beneficial for various optimization purposes, as demonstrated in [24] where it was utilized for optimizing type level model queries. Additionally, rewriting has played a crucial role in translating EOL expressions to Viatra for incremental evaluation [6], as well as converting them to MySQL queries for efficient execution on relational databases [7].…”

Section: Related Workmentioning

confidence: 99%

Towards Efficient Model Comparison using Automated Program Rewriting

Ali,

Kolovos,

Barmpis

2023

Proceedings of the 16th ACM SIGPLAN International Conference on Software Language Engineering

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Model comparison is a prerequisite task for several other model management tasks such as model merging, model differencing etc. We present a novel approach to efficiently compare models using programs written in a rule-based model comparison language. As the comparison is done at the model element level, and each element needs to be traversed and compared with its corresponding elements, the execution of these comparison algorithms can be computationally expensive for larger models. In this paper, we present an efficient comparison approach which provides an automated rewriting facility to compare (both homogeneous and heterogeneous) models, based on static program analysis. Using this analysis, we reduce the search space by pre-filtering/indexing model elements, before actually comparing them. Moreover, we reorder the comparison match rules according to the dependencies between these rules to reduce the cost of jumping between rules. Our experiments demonstrate that the proposed model comparison approach delivers significant performance benefits in terms of execution time compared to the default ECL execution engine.CCS Concepts: • Software and its engineering → Domain specific languages.

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“…Static analysis has been used for enabling the translation from EOL to SQL [9], for optimisation of programs over EMF models [27] and for enabling the translation from EOL to Viatra patterns [8].…”

Section: Related Workmentioning

confidence: 99%

Selective Traceability for Rule-Based Model-to-Model Transformations

Ali

Kolovos

Barmpis

2022

Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering

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Model-to-model (M2M) transformation is a key ingredient in a typical Model-Driven Engineering workflow and there are several tailored high-level interpreted languages for capturing and executing such transformations. While these languages enable the specification of concise transformations through task-specific constructs (rules/mappings, bindings), their use can pose scalability challenges when it comes to very large models. In this paper, we present an architecture for optimising the execution of model-to-model transformations written in such a language, by leveraging static analysis and automated program rewriting techniques. We demonstrate how static analysis and dependency information between rules can be used to reduce the size of the transformation trace and to optimise certain classes of transformations. Finally, we detail the performance benefits that can be delivered by this form of optimisation, through a series of benchmarks performed with an existing transformation language (Epsilon Transformation Language -ETL) and EMF-based models. Our experiments have shown considerable performance improvements compared to the existing ETL execution engine, without sacrificing any features of the language.CCS Concepts: • Software and its engineering → Modeldriven software engineering.

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Identification and Optimisation of Type-Level Model Queries

Cited by 3 publications

References 12 publications

Optimizing the Execution of Model Transformation Chain

Optimizing the Execution of Model Transformation Chain

Towards Efficient Model Comparison using Automated Program Rewriting

Selective Traceability for Rule-Based Model-to-Model Transformations

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