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de Lara, Juan

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juan.delara@uam.es

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de Lara

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Juan

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De Lara, Juan

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Universidad Autónoma de Madrid, Spain

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Mostrando 1 - 5 de 5
  • Artículo
    Towards a Deep Learning Architecture for Software Models: An Initial Exploration
    Mata, Luis; de Lara, Juan; Guerra, Esther. Actas de las XXVI Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2022), 2022-09-05.
    As in many other research areas, the use of Deep Learning (DL) techniques is growing in software engineering. However, these techniques are not yet widespread in the Model-Driven Engineering (MDE) field. In this paper, we explore the use of DL to extract useful text embeddings out of software models. We propose a novel approach to embedding software models by means of transformer architectures trained on large datasets. Our approach combines intermediate representations and Language Models (LMs) to extract features from modelling artefacts in order to enable applications of interest, like intelligent model assistance, classification, transformation, completion and correction, among others. We show that the approach is potentially useful in MDE and may lead to useful results in the future.
  • Artículo
    Análisis de transformaciones de modelos ATL con AnATLyzer
    Sánchez Cuadrado, Jesús; Guerra, Esther; de Lara, Juan. Actas de las XXI Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2016), 2016-09-13.
    Las transformaciones de modelos son un elemento clave del Desarrollo de Software Dirigido por Modelos puesto que permiten automatizar muchas tareas de manipulación de modelos. Por tanto, disponer de métodos que permitan detectar errores no triviales resulta esencial. Sin embargo no existen herramientas prácticas de análisis de transformaciones que sean capaces de tratar con transformaciones complejas. En esta demostración se presentará anATLyzer un analizador estático para transformaciones ATL que hace uso de "constraint solving" para mejorar la precisión del análisis. AnATLyzer no se limita a un subconjunto de ATL sino que intenta cubrir ATL completamente. Se integra con el editor de ATL en Eclipse, y ofrece servicios adicionales como visualización y quick fixes, así como una API para ser utilizado de manera programática. La demostración se ilustrará con un ejemplo sobre el que se mostrarán algunos de los tipos de errores que hemos encontrado analizando transformaciones del Zoo de ATL, con el objetivo de motivar la necesidad de este tipo de herramientas y mostrar sus características principales.
  • Artículo
    Towards a model-driven engineering solution for language independent mutation testing
    Gómez-Abajo, Pablo; Guerra, Esther; de Lara, Juan; Merayo, Mercedes G.. Actas de las XXIII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2018), 2018-09-17.
    Mutation testing is a technique to assess test suite adequacy to distinguish between correct and incorrect programs. Mutation testing applies one or more small changes to a program to obtain variants called mutants. The adequacy of a test suite is measured by determining how many of the mutants it distinguishes from the original program. There are many works about mutation testing, but the existing approaches focus on a specific programming language, and usually, it is not easy to customize the set of mutation operators. In this paper, we present Wodel-Test, an extension of the Wodel tool that implements a language-independent mutation testing framework based on model-driven engineering principles.
  • Artículo
    Building Scalable Graphical Modelling Environments with EMFSplitter (tool demo)
    Garmendia, Antonio; Guerra, Esther; de Lara, Juan. Actas de las XXIII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2018), 2018-09-17.
    In Model-Driven Engineering the creation of Domain-Specific Modelling Languages (DSMLs) is a recurrent demanding task. Usually DSMLs are built in an ad-hoc manner and the generated environments do not scale well to face scenarios with complex systems. To improve this situation, we propose an approach to facilitate the engineering of DSMLs through a catalogue of patterns and a set of wizards to reduce the implementation time of such environments. Our approach is supported by a tool called EMFSplitter, which proposes a Modularity pattern to fragment the models and a Graphical Representation pattern, for the definition of graphical and tabular syntax.
  • Artículo
    Towards an Extensible Architecture for LLM-based Programming Assistants in IDEs
    Contreras Romero, Albert; Guerra, Esther; de Lara, Juan. Actas de las XXVIII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2024), 2024-06-17.
    Large Language Models (LLMs) are the backbone of chatbots like ChatGPT, and are used to assist in all sort of domains. Following this trend, we are witnessing proposals of LLM-based assistants for coding tasks. However, current IDEs lack mechanisms tailored to facilitate the integration of such assistants, from how to interact with them to how to apply their suggestions without leaving the environment. To fill this gap, this short paper presents an extensible architecture for the definition of assistance tasks (e.g., method renaming) based on LLMs, and their binding to IDE commands and natural language prompts. We report on an ongoing effort to build a Java assistant within Eclipse based on this architecture, and illustrate its use.