Identification and Optimisation of Type-Level Model Queries

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 general purpose 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%

Efficiently Querying Large-Scale Heterogeneous Models

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

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

Consistency-by-Construction Techniques for Software Models and Model Transformations

A model is consistent with given specifications (specs) if and only if all the specifications are held on the model, i.e., all the specs are true (correct) for the model. Constructing consistent models (e.g., programs or artifacts) is vital during software development, especially in Model-Driven Engineering (MDE), where models are employed throughout the life cycle of software development phases (analysis, design, implementation, and testing). Models are usually written using domain-specific modeling languages (DSMLs) and specified to describe a domain problem or a system from different perspectives and at several levels of abstraction. If a model conforms to the definition of its DSML (denoted usually by a meta-model and integrity constraints), the model is consistent. Model transformations are an essential technology for manipulating models, including, e.g., refactoring and code generation in a (semi)automated way. They are often supposed to have a well-defined behavior in the sense that their resulting models are consistent with regard to a set of constraints. Inconsistent models may affect their applicability and thus the automation becomes untrustworthy and error-prone. The consistency of the models and model transformation results contribute to the quality of the overall modeled system. Although MDE has significantly progressed and become an accepted best practice in many application domains such as automotive and aerospace, there are still several significant challenges that have to be tackled to realize the MDE vision in the industry. Challenges such as handling and resolving inconsistent models (e.g., incomplete models), enabling and enforcing model consistency/correctness during the construction, fostering the trust in and use of model transformations (e.g., by ensuring the resulting models are consistent), developing efficient (automated, standardized and reliable) domain-specific modeling tools, and dealing with large models are continually making the need for more research evident. In this thesis, we contribute four automated interactive techniques for ensuring the consistency of models and model transformation results during the construction process. The first two contributions construct consistent models of a given DSML in an automated and interactive way. The construction can start at a seed model being potentially inconsistent. Since enhancing a set of transformations to satisfy a set of constraints is a tedious and error-prone task and requires high skills related to the theoretical foundation, we present the other contributions. They ensure model consistency by enhancing the behavior of model transformations through automatically constructing application conditions. The resulting application conditions control the applicability of the transformations to respect a set of constraints. Moreover, we provide several optimizing strategies. Specifically, we present the following: First, we present a model repair technique for repairing models in an automated and interactive way. Our approach guides the modeler to repair the whole model by resolving all the cardinalities violations and thereby yields a desired, consistent model. Second, we introduce a model generation technique to efficiently generate large, consistent, and diverse models. Both techniques are DSML-agnostic, i.e., they can deal with any meta-models. We present meta-techniques to instantiate both approaches to a given DSML; namely, we develop meta-tools to generate the corresponding DSML tools (model repair and generation) for a given meta-model automatically. We present the soundness of our techniques and evaluate and discuss their features such as scalability. Third, we develop a tool based on a correct-by-construction technique for translating OCL constraints into semantically equivalent graph constraints and integrating them as guaranteeing application conditions into a transformation rule in a fully automated way. A constraint-guaranteeing application condition ensures that a rule applies successfully to a model if and only if the resulting model after the rule application satisfies the constraint. Fourth, we propose an optimizing-by-construction technique for application conditions for transformation rules that need to be constraint-preserving. A constraint-preserving application condition ensures that a rule applies successfully to a consistent model (w.r.t. the constraint) if and only if the resulting model after the rule application still satisfies the constraint. We show the soundness of our techniques, develop them as ready-to-use tools, evaluate the efficiency (complexity and performance) of both works, and assess the overall approach in general as well. All our four techniques are compliant with the Eclipse Modeling Framework (EMF), which is the realization of the OMG standard specification in practice. Thus, the interoperability and the interchangeability of the techniques are ensured. Our techniques not only improve the quality of the modeled system but also increase software productivity by providing meta-tools for generating the DSML tool supports and automating the tasks

Engineering scalable modelling Languages

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 08-11-2019Esta tesis tiene embargado el acceso al texto completo hasta el 08-05-2021Model-Driven Engineering (MDE) aims at reducing the cost of system development by raising the level of abstraction at which developers work. MDE-based solutions frequently involve the creation of Domain-Specific Modelling Languages (DSMLs). WhilethedefinitionofDSMLsandtheir(sometimesgraphical)supportingenvironments are recurring activities in MDE, they are mostly developed ad-hoc from scratch. The construction of these environments requires high expertise by developers, which currently need to spend large efforts for their construction. This thesis focusses on the development of scalable modelling environments for DSMLs based on patterns. For this purpose, we propose a catalogue of modularity patterns that can be used to extend a modelling language with services related to modularization and scalability. More specifically, these patterns allows defining model fragmentation strategies, scoping and visibility rules, model indexing services, and scoped constraints. Once the patterns have been applied to the meta-model of a modelling language, we synthesize a customized modelling environment enriched with the defined services, which become applicable to both existing monolithic legacy models and new models. A second contribution of this thesis is a set of concepts and technologies to facilitate the creation of graphical editors. For this purpose, we define heuristics which identify structures in the DSML abstract syntax, and automatically assign their diagram representation. Using this approach, developers can create a graphical representation by default from a meta-model, which later can be customised. These contributions have been implemented in two Eclipse plug-ins called EMFSplitter and EMF-Stencil. On one hand, EMF-Splitter implements the catalogue of modularity patterns and, on the other hand, EMF-Stencil supports the heuristics and the generation of a graphical modelling environment. Both tools were evaluated in different case studies to prove their versatility, efficiency, and capabilitieEl Desarrollo de Software Dirigido por Modelos (MDE, por sus siglas en inglés) tiene como objetivo reducir los costes en el desarrollo de aplicaciones, elevando el nivel de abstracciónconelqueactualmentetrabajanlosdesarrolladores. Lassolucionesbasadas en MDE frecuentemente involucran la creación de Lenguajes de Modelado de Dominio Específico (DSML, por sus siglas en inglés). Aunque la definición de los DSMLs y sus entornos gráficos de modelado son actividades recurrentes en MDE, actualmente en la mayoría de los casos se desarrollan ad-hoc desde cero. La construcción de estos entornos requiere una alta experiencia por parte de los desarrolladores, que deben realizar un gran esfuerzo para construirlos. Esta tesis se centra en el desarrollo de entornos de modelado escalables para DSML basados en patrones. Para ello, se propone un catálogo de patrones de modularidad que se pueden utilizar para extender un lenguaje de modelado con servicios relacionados con la modularización y la escalabilidad. Específicamente, los patrones permiten definir estrategias de fragmentación de modelos, reglas de alcance y visibilidad, servicios de indexación de modelos y restricciones de alcance. Una vez que los patrones se han aplicado al meta-modelo de un lenguaje de modelado, se puede generar automáticamente un entorno de modelado personalizado enriquecido con los servicios definidos, que se vuelven aplicables tanto a los modelos monolíticos existentes, como a los nuevos modelos. Una segunda contribución de esta tesis es la propuesta de conceptos y tecnologías para facilitar la creación de editores gráficos. Para ello, definimos heurísticas que identifican estructuras en la sintaxis abstracta de los DSMLs y asignan automáticamente su representación en el diagrama. Usando este enfoque, los desarrolladores pueden crear una representación gráfica por defecto a partir de un meta-modelo. Estas contribuciones se implementaron en dos plug-ins de Eclipse llamados EMFSplitter y EMF-Stencil. Por un lado, EMF-Splitter implementa el catálogo de patrones y, por otro lado, EMF-Stencil implementa las heurísticas y la generación de un entorno de modelado gráfico. Ambas herramientas se han evaluado con diferentes casos de estudio para demostrar su versatilidad, eficiencia y capacidade

Arquitetura MCGML : classificando modelos desestruturados usando aprendizado de máquina em grafos

Orientador: Andrey Ricardo PimentelTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/03/2023Inclui referênciasÁrea de concentração: Ciência da ComputaçãoResumo: Modelos e metamodelos criados usando abordagens baseadas em modelos têm relações de conformidades restritas. No entanto, houve um aumento nos formatos de dados livres de schemas ou semiestruturados, tais como as representações orientadas a documentos, as quais geralmente são persistidas como documentos JSON. Apesar de não terem um metamodelo/schema explícitos, esses documentos poderiam ser categorizados visando obter conhecimento sobre seus domínios, e conformá-los parcialmente aos seus respectivos metamodelos. Abordagens recentes estão surgindo objetivando extrair informações ou combinar soluções de modelagem com cognificação. Existe porém uma carência de abordagens que explorem a classificação de formatos semiestruturados. Esta tese aborda justamente essas limitações, apresentando a Arquitetura MCGML (Model Classification using Graph Machine Learning): uma abordagem para analisar e classificar modelos desestruturados usando Aprendizado de Máquina em Grafos. Primeiro descrevemos nossa primeira etapa de pesquisa que diz respeito ao processo de extração de elementos de um metamodelo para dentro de uma rede neural Multi-Layer Perceptron (MLP) para que a mesma possa ser treinada. Em seguida convertemos documentos JSON em um formato de entrada codificado para a MLP. Apresentaremos nesta tese, o passo a passo para classificar documentos JSON de acordo com metamodelos existentes extraídos a partir de um repositório. Em seguida apresentamos a Arquitetura MCGML com o objetivo de classificar e analisar similaridades em modelos e metamodelos através da análise de grafos e os algoritmos de Machine Learning (ML). Mostramos também uma série de experimentos para demonstrar a viabilidade da arquitetura MCGML e sua efetividade em classificar documentos.Abstract: Models and metamodels created using model-based approaches have restrict conformance relations. However, there has been an increase of semi-structured or schema-free data formats, such as document-oriented representations, which are often persisted as JSON documents. Despite not having an explicit schema/metamodel, these documents could be categorized to discover their domain and to partially conform to a metamodel. Recent approaches are emerging to extract information or to couple modeling with cognification. However, there is a lack of approaches exploring semi-structured formats classification. In this thesis, we address precisely these limitations, we present MCGML Architecture (Model Classification using Graph Machine Learning): an approach to analyze and classify unstructured models using Graph Machine Learning. First, we describe our first research stage that concerns how to extract metamodels elements into a Multi-Layer Perceptron (MLP) network to be trained. Then, we translate the JSON documents into the input format of the encoded MLP. We present the step-by-step tasks to classify JSON documents according to existing metamodels extracted from a repository. Then we present the MCGML Architecture in order to classify and analyze similarities in models and metamodels through graph analysis and Machine Learning (ML) algorithms. We have conducted a set of experiments, showing that the approach is feasible, and its effectiveness in classifying documents

Fundamental Approaches to Software Engineering

computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio