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With the increase in the complexity of software systems, the size and the complexity of underlying models also increases proportionally. In a low-code system, models can be stored in different backend technologies and can be represented in various formats. Tailored high-level query languages are used to query such heterogeneous models, but typically this has a significant impact on performance. Our main aim is to propose optimization strategies that can help to query large models in various formats efficiently. In this paper, we present an approach based on compile-time static analysis and specific query optimizers/translators to improve the performance of complex queries over large-scale heterogeneous models. The proposed approach aims to bring efficiency in terms of query execution time and memory footprint, when compared to the naive query execution for low-code platforms. CCS CONCEPTS • Software and its engineering → Model-driven software 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.

The main appeal of task-specific model management languages such as ATL, OCL, Epsilon etc. is that they offer tailored syntaxes for the tasks they target, and provide concise first-class support for recurring activities in these tasks. On the flip side, task-specific model management languages are typically interpreted and are therefore significantly slower than generalpurpose programming languages (which can be also used to query and modify models) such as Java. While this is not an issue for smaller models, as models grow in size, naive execution of interpreted model management programs against them can become a scalability bottleneck. In this paper, we demonstrate an architecture for optimisation of model management programs written in languages of the Epsilon platform using static analysis and program rewriting techniques. The proposed architecture facilitates optimisation of queries that target models of heterogeneous technologies in an orthogonal way. We demonstrate how the proposed architecture is used to identify and optimise typelevel queries against EMF-based models in the context of EOL programs and EVL validation constraints. We also demonstrate the performance benefits that can be delivered by this form of optimisation through a series of experiments on EMF-based models. Our experiments have shown performance improvements of up to 99.56%.