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

TY - GEN

T1 - Selective Traceability for Rule-Based Model-to-Model Transformations

AU - Ali, Qurat Ul Ain

AU - Kolovos, Dimitris

AU - Barmpis, Konstantinos

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2022/12/1

Y1 - 2022/12/1

N2 - 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.

AB - 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.

KW - Model-Driven Engineering

KW - Scalability

KW - Model Transformation

KW - Static Analysis

U2 - 10.1145/3567512.3567521

DO - 10.1145/3567512.3567521

M3 - Conference contribution

SN - 9781450399197

SP - 98

EP - 109

BT - Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022)

PB - ACM

T2 - Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022)

Y2 - 6 November 2022 through 7 November 2022

ER -