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El autor Loli Burgueño ha publicado 10 artículo(s):

1 - Analysis of the Scientific Production of the Spanish Software Engineering Community

Our group has been working on a report for the SpanishúSociety of Software Engineering and Software Development Technologies (SISTEDES) to provide a general overview of the Spanish scientificúproduction and its contributions worldwide in the field of Software Engineering. Although a Database solution could have been used, we decidedúto employ Model-Driven Development (MDD) techniques in order toúevaluate their applicability, suitability and fitness for these kinds of purposes, and to learn from the experience in this domain, which combinesúdata integration, large scale models, and complex queries.

Autores: Loli Burgueño / Antonio Moreno-Delgado / Antonio Vallecillo / 
Palabras Clave: MDD - scientific contribution - SISTEDES - software engineering - Spain

2 - 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.

Autores: Loli Burgueño / Antonio Vallecillo / Eugene Syriani / Jeff Gray / Manuel Wimmer / 
Palabras Clave: LinTra - Model Transformation - Primitives

3 - Concurrent Model Transformations with Linda

Nowadays, model transformations languages and engines use a sequential execution model. This is, only one execution thread deals with the whole transformation. However, model transformations dealing with very large models, such as those used in biology or aerospace applications, require concurrent solutions in order to speed up their performance. In this ongoing work we explore the use of Linda for implementing a set of basic mechanisms to enable concurrent model transformations, and present our initial results.

Autores: Loli Burgueño / Javier Troya / Antonio Vallecillo / 
Palabras Clave: Concurrency - Linda - Model Transformation

4 - Prueba de Transformaciones de Modelos con TractsTool

Las tranformaciones de modelos son un elemento esencial en la Ingeniería Dirigida por Modelos (Model-driven Engineering, MDE) y por ello una tarea que está cobrando relevancia es probar su corrección. Los Tracts ofrecen un enfoque modular y extensible para la especificación y verificación de transformaciones de modelos. Este trabajo presenta TractsTool, una herramienta desarrollada en Eclipse que implementa los mecanismos que proporcionan los Tracts.

Autores: Manuel Wimmer / Loli Burgueño / Antonio Vallecillo / 
Palabras Clave: MDE - Tracts - Transformaciones de Modelos

6 - A Linda-based Platform for the Parallel Execution of Out-place Model Transformations

Context: The performance and scalability of model transformations is gaining interest as industry is progressively adopting model-driven techniques and multicore computers are becoming commonplace. However, existing model transformation engines are mostly based on sequential and in-memory execution strategies, and thus their capabilities to transform large models in parallel and distributed environments are limited. Objective: This paper presents a solution that provides concurrency and distribution to model transformations. Method: Inspired by the concepts and principles of the Linda coordination language, and the use of data parallelism to achieve parallelization, a novel Java-based execution platform is introduced. It offers a set of core features for the parallel execution of out-place transformations that can be used as a target for high-level transformation language compilers. Results: Significant gains in performance and scalability of this platform are reported with regard to existing model transformation solutions. These results are demonstrated by running a model transformation test suite, and by its comparison against several state-of-the-art model transformation engines. Conclusion: Our Linda-based approach to the concurrent execution of model transformations can serve as a platform for their scalable and efficient implementation in parallel and distributed environments.

Autores: Loli Burgueño / Manuel Wimmer / Antonio Vallecillo / 
Palabras Clave: Model Transformation - Parallelization - Performance - Scalability

7 - Formally Modeling, Executing, and Testing Service-Oriented Systems with UML and OCL (YA PUBLICADO)

One of the issues that developers of service-oriented systems currently discuss is the lack of practical, but formal modeling notations and tools that can address the many different, important aspects. This paper presents an approach to model structural and behavioral properties of service-oriented systems with UML and OCL models. Essential service-oriented concepts as service request, service provision or orchestration are formally represented by UML concepts. The models can be executed, tested and analyzed. Feedback is given to the developer in terms of the UML and OCL model.Our approach supports the automatic generation of test scenarios in which, for example, the availability of services can be checked. Furthermore, the consistency of the service model can be proved by constructing test scenarios.

Autores: Loli Burgueño / Martin Gogolla / 
Palabras Clave: Formal modeling - Service-oriented systems - UML/OCL

8 - Confianza e Incertidumbre en Modelos y Transformaciones de Modelos

La incertidumbre, tanto en los datos como en los mecanismos que manipulan y operan sobre ellos, es un tema crucial en sistemas que trabajan con entornos físicos. Una incertidumbre que puede ser debida a diversos factores, como fuentes de datos poco fiables, tolerancia en las mediciones o la incapacidad para determinar si un determinado evento ha sucedido realmente o no. En este trabajo proponemos el uso de modelos con confianza, donde los objetos pueden llevar asociadas probabilidades. Al igual que en los modelos, la incertidumbre puede trasladarse a las transformaciones de modelos, donde las reglas también pueden estar sujetas a incertidumbre.

Autores: Loli Burgueño / Gala Barquero / Nathalie Moreno / Manuel F. Bertoa / Antonio Vallecillo / 
Palabras Clave: Confianza - Incertidumbre - MDE - Modelos - Transformaciones de Modelos

9 - Testing Models and Model Transformations Using Classifying Terms

Título: Testing Models and Model Transformations Using Classifying TermsAutores: Frank Hilken, Martin Gogolla, Loli Burgueño, Antonio VallecilloRevista: Software and System Modeling (Sosym)Número: 17(3)Fecha de publicación: Julio 2018Páginas: 885-912DOI: 10.1007/s10270-016-0568-3Indicios de calidad:-Factor de impacto: 1.722 (Q2)- Número de citas: 16 (según GSholar)

Autores: Frank Hilken / Martin Gogolla / Loli Burgueño / Antonio Vallecillo / 
Palabras Clave: Contract-based specifications - Equivalence partitioning - model transformations

10 - Transformaciones de Datos con Machine Learning

Una de las tareas más comunes que los ingenieros tienen que llevar a cabo y que consumen más tiempo es la transformación de datos. Proponemos usar los avances en Inteligencia Artificial (IA), y en particular, en el área de Machine Learning (ML), para abordar este problema. Para ello, definimos una arquitectura que es capaz de inferir las transformaciones de datos a partir de un conjunto de pares de datos entrada-salida. Una vez que nuestro sistema haya aprendido cómo los datos de entrada se relacionan con los de salida, podrá realizar la traducción de nuevos datos de entrada automáticamente.

Autores: Loli Burgueño / Jordi Cabot / Sébastien Gérard / 
Palabras Clave: Machine Learning - MDE - Transformación de datos