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

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

doi:10.1145/3567512.3567521

Cited by 2 publications

(3 citation statements)

References 22 publications

(22 reference statements)

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“…We have used the class and sequence diagram models of different sizes conforming to these metamodels publicly available on GitHub [13]. OO & DB are the synthetic models generated in [8]. The number of elements of different models are mentioned in Table 4.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

“…Due to these rule invocations, rules can be dependent on one another. These dependencies can be extracted by the help of the type resolved AST, as done for model-to-model transformations in[8]. To construct a dependency graph, we create a vertex for each MatchRule declared in the ECL program.…”

mentioning

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

Self Cite

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show abstract

“…The static analyzer first invokes the algorithm presented in [11], in charge of building the dependency graph between the rules based on the equivalent(s) operator used in statements of those rules [12]. The equivalent operator is a built-in operator of ETL which automatically resolves features of source elements to their transformed counterparts in the target models.…”

Section: Static Analyzermentioning

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

Cited by 2 publications

References 22 publications

Towards Efficient Model Comparison using Automated Program Rewriting

Towards Efficient Model Comparison using Automated Program Rewriting

Optimizing the Execution of Model Transformation Chain

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