Parallel In-place Model Transformations with LinTra

As software systems have grown large and complex in the last few years, the problems with which Model-Driven Development has to cope have increased at the same pace. In particular, the need to improve the performance and scalability of model transformations has become a critical issue. In previous work we introduced LinTra, a model transformation platform for the parallel execution of out-place model transformations. Nevertheless, in-place model transformations are required in several contexts and domains as well. In this paper we discuss the fundamentals of in-place model transformations in the light of their parallel execution and provide LinTra with an in-place execution mode.Ministerio de Ciencia e Innovación TIN2011-23795Ministerio de Economía y Competitividad TIN2014-52034-REuropean Commission ICT Policy Support Programme 31785

Towards Distributed Model Transformations with LinTra

Performance and scalability of model transformations are becoming prominent topics in Model-Driven Engineering. In previous works we introduced LinTra, a platform for executing model transformations in parallel. LinTra is based on the Linda model of a coordination language and is intended to be used as a middleware where high-level model transformation languages are compiled. In this paper we present the initial results of our analyses on the scalability of out-place model-to-model transformation executions in LinTra when the models and the processing elements are distributed over a set of machines.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

LinTraP: Primitive Operators for the Execution of Model Transformations with LinTra

The problems addressed by Model-Driven Engineering (MDE) approaches are increasingly complex, hence performance and scalability of model transformations are gaining importance. In previous work, we introduced LinTra, which is a platform for executing out-place model transformations in parallel. The parallel execution of LinTra is based on the Linda coordination language, where high-level model transformation languages (MTLs) are compiled to LinTra and eventually executed through Linda. In order to de ne the compilation modularly, this paper presents a minimal, yet su cient, collection of primitive operators that can be composed to (re-)construct any out-place, unidirectional MTL.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tech. Spanish Project TIN2011-2379

Primitive Operators for the Concurrent Execution of Model Transformations Based on LinTra

Performance and scalability of model transformations are becoming prominent topics in Model-Driven Engineering. In previous work, we introduced LinTra, a platform for executing out-place model transformations in parallel. LinTra is based on the Linda coordination language for archiving concurrency and distribution and is intended to be used as a middleware where high-level model transformation languages (such as ATL and QVT) are compiled. To define modularly the compilation, this paper presents a minimal, yet sufficient, collection of primitive operators that can be composed to (re-)construct any out place, unidirectional model transformation language (MTL). These primitives enable any MTL to be executed in parallel in a transparent way, without altering the original transformation.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

On the Quality Properties of Model Transformations: Performance and Correctness

The increasing complexity of software due to continuous technological advances has motivated the use of models in the software development process. Initially, models were mainly used as drafts to help developers understand their programs. Later they were used extensively and a new discipline called Model-Driven Engineering (MDE) was born. In the MDE paradigm, aside from the models themselves, model transformations (MT) are garnering interest as they allow the analysis and manipulation of models. Therefore, the performance, scalability and correctness of model transformations have become critical issues and thus they deserve a thorough study. Existing model transformation engines are principally based on sequential and in-memory execution strategies, and hence their capabilities to transform very large models in parallel and in distributed environments are limited. Current tools and languages are not able to cope with models that are not located in a single machine and, even worse, most of them require the model to be in a single file. Moreover, once a model transformation has been written and executed-either sequentially or in parallel-it is necessary to rely on methods, mechanisms, and tools for checking its correctness. In this dissertation, our contribution is twofold. Firstly, we introduce a novel execution platform that permits the parallel execution of both out-place and in-place model transformations, regardless of whether the models fit into a single machine memory or not. This platform can be used as a target for high-level transformation language compilers, so that existing model transformations do not need to be rewritten in another language but only have to be executed more efficiently. Another advantage is that a developer who is familiar with an existing model transformation language does not need to learn a new one. In addition to performance, the correctness of model transformations is an essential aspect that needs to be addressed if MTs are going to be used in realistic industrial settings. Due to the fact that the most popular model transformation languages are rule-based, i.e., the transformations written in those languages comprise rules that define how the model elements are transformed, the second contribution of this thesis is a static approach for locating faulty rules in model transformations. Current approaches able to fully prove correctness-such as model checking techniques-require an unacceptable amount of time and memory. Our approach cannot fully prove correctness but can be very useful for identifying bugs at an early development stage, quickly and cost effectively

Analysis of the scientific production of the Spanish Software Engineering Community

Our group has been working on a report for the Spanish Society of Software Engineering and Software Development Technologies (SISTEDES) to provide a general overview of the Spanish scientific production and its contributions worldwide in the field of Software Engineering. Although a Database solution could have been used, we decided to employ Model-Driven Development (MDD) techniques in order to evaluate their applicability, suitability and fitness for these kinds of purposes, and to learn from the experience in this domain, which combines data integration, large scale models, and complex queries.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis of the Scientific Production of the Spanish Software Engineering Community

Our group has been working on a report for the Spanish Society of Software Engineering and Software Development Technologies (SISTEDES) to provide a general overview of the Spanish scientific production and its contributions worldwide in the field of Software Engineering. Although a Database solution could have been used, we decided to employ Model-Driven Development (MDD) techniques in order to evaluate their applicability, suitability and fitness for these kinds of purposes, and to learn from the experience in this domain, which combines data integration, large scale models, and complex queries.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Towards Transparent Combination of Model Management Execution Strategies for Low-Code Development Platforms

International audienceLow-code development platforms are taking an important place in the model-driven engineering ecosystem, raising new challenges, among which transparent efficiency or scalability. Indeed, the increasing size of models leads to difficulties in interacting with them efficiently. To tackle this scalability issue, some tools are built upon specific computational strategies exploiting reactivity, or parallelism. However, their performances may vary depending on the specific nature of their usage. Choosing the most suitable computational strategy for a given usage is a difficult task which should be automated. Besides, the most efficient solutions may be obtained by the use of several strategies at the same time. is paper motivates the need for a transparent multi-strategy execution mode for model-management operations. We present an overview of the different computational strategies used in the model-driven engineering ecosystem, and use a running example to introduce the benefits of mixing strategies for performing a single computation. is example helps us present our design ideas for a multi-strategy model-management system. e code-related and DevOps challenges that emerged from this analysis are also presented

DC4MT : uma abordagem orientada a dados para transformação de modelos

Orientador: Marcos Didonet Del FabroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 25/08/2020Inclui referências: p. 95-106Área de concentração: Ciência da ComputaçãoResumo: Transformações de Modelos são operações que recebem um conjunto de modelos como entrada e produzem um conjunto de modelos como saída, seguindo uma especificação. Há uma coleção diversificada de abordagens e ferramentas utilizadas para a especificação de diferentes tipos de transformações de modelos. A maioria dessas abordagens adota como estratégia a execução local e sequencial. No entanto, essas abordagens não estão totalmente aptas para processar modelos com grandes quantidades de elementos. VLMs (Very Large Models) são modelos que possuem milhões de elementos. Esses modelos estão presentes em domínios de aplicações como na indústria automotiva, modernização de sistemas legados, internet das coisas, redes sociais, entre outros domínios. Essas abordagens possuem lacunas para suportar o processamento desses VLMs. Por exemplo, para possibilitar a execução das transformações de modelos, considerando a escala do problema ou para melhoria de desempenho. Nesta tese é proposta a Dc4MT, uma abordagem para suportar transformações de VLMs com a aplicação e adaptação de técnicas relacionadas à distribuição de dados. A Dc4MT é uma abordagem Orientada a Dados (Dc - Data-centric) para ser aplicada no domínio da Engenharia Dirigida por Modelos (MDE - Model Driven Engineering). A abordagem é especificada, utilizando um framework de processamento distribuído, e define um conjunto de operações para a fragmentação, extração e transformação de modelos. A fragmentação é uma operação que divide os modelos de entrada (em formatos XMI ou JSON) em fragmentos, de modo que esses fragmentos possam ser distribuídos e processados de maneira paralela/distribuída. A extração é uma operação que processa os fragmentos do modelo de entrada e os traduz em um grafo acíclico, atribuindo um novo domínio de modelagem a esses fragmentos. A transformação de modelos na abordagem Dc4MT é uma operação que transforma modelos de entrada em modelos de saída (M2M) a partir do resultado da extração. As execuções de transformação podem ser em modo paralelo ou distribuído, com ou sem a intervenção no método de particionamento do framework disponível para melhorar o desempenho. Um conjunto de modelos de entrada (datasets) e os ambientes local (transformações paralelas) e distribuído (transformações distribuídas) são utilizados nos experimentos para validar a abordagem Dc4MT, sob os aspectos de factibilidade, desempenho e de escalabilidade. Os resultados desses experimentos, mostram que as operações de fragmentação e extração de modelos favorecem a transformação escalável de VLMs, reconstruindo a estrutura dos fragmentos em um grafo. A operação de extração é executada em modo paralelo/distribuído. Além disso, os aspectos como a imutabilidade, lazy-evaluate e o paralelismo implícito presentes na Dc4MT, permitem o processamento paralelo/distribuído de regras de transformação em uma plataforma escalável. Palavras-chave: Abordagem Orientada a Dados. Engenharia Dirigida por Modelos. Transformação Paralela de Modelos. Transformação Distribuída de Modelos.Abstract: Model Transformations are operations that receive a set of source models as input and produce a set of target models as output, following a specification. There is a variety of approaches and tools used for the specification of different types of model transformation. Most of these approaches adopt for model transformation the local and sequential execution strategy. However, these approaches not fully adapted for processing models with large amounts of elements. VLMs (Very Large Models) are models with millions of elements. These models are present in application domains such as the automotive industry, modernization of legacy systems, internet of things, social networks, among others. These approaches have gaps to support the processing of these increasingly larger models. For example, to enable model transformations, considering the scale of the problem or to improve performance. In this thesis, the Dc4MT is proposed such as an approach to support transformation of VLMs, applying and adapting distribution techniques of data. The Dc4MT is a Data-centric (Dc) approach for applying in Model Driven Engineering (MDE). The approach will be specified using a distributed processing framework, and defines a set of operations for fragmentation, extraction, and transformation of models. The fragmentation is an operation that splits the input models (in the XMI or JSON formats) in a way that the fragments can be processed in parallel/distributed. The extraction is an operation that processes the fragments of the input model in parallel and translates them to an acyclic graph, assigning a new modeling domain to these fragments. The model transformation in Dc4MT is an operation that transforms input models in output models (M2M) from the results of the extraction. The transformation executions can be parallel or distributed with ou without the intervention in the framework partitioning method to improve the performance. A set of input models (datasets) and the local (parallel transformations) and distributed (distributed transformations) environments are used in the experiments to validate the Dc4MT approach, in terms of feasibility, performance, and scalability. The results of the experiments show that the model fragmentation and extraction operations favor the scalable transformation of models, reconstructing the structure of the fragments in a graph. The extraction operation is executed on parallel/distributed way. Moreover, aspects such as immutability, lazy-evaluation, and implicit parallelism present in Dc4MT, allowing the parallel/distributed processing of transformation rules on a scalable platform. Keywords: Data-centric Approach. Model Driven Engineering. Parallel Model Transformation. Distributed Model Transformation

Parallel model validation with epsilon

Traditional model management programs, such as transformations, often perform poorly when dealing with very large models. Although many such programs are inherently parallelisable, the execution engines of popular model management languages were not designed for concurrency. We propose a scalable data and rule-parallel solution for an established and feature-rich model validation language (EVL). We highlight the challenges encountered with retro-fitting concurrency support and our solutions to these challenges. We evaluate the correctness of our implementation through rigorous automated tests. Our results show up to linear performance improvements with more threads and larger models, with significantly faster execution compared to interpreted OCL