Resultados de búsqueda para Model-Driven Engineering

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.

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms, and they are also increasingly applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

Security policies constrain the behaviour of all users of an information system. In any non-trivial system, these security policies go beyond simple access control rules and must cover more complex and dynamic scenarios while providing, at the same time, a fine-grained level decision-making ability. The Usage Control model (UCON) was created for this purpose but so far integration of UCON in mainstream software engineering processes has been very limited, hampering its usefulness and popularity among the software and information systems communities. In this sense, this paper proposes a Domain-Specific Language to facilitate the modelling of UCON policies and their integration in (model-based) development processes. Together with the language, an exploratory approach for policy evaluation and enforcement of the modeled policies via model transformations has been introduced. These contributions have been defined on top of the Eclipse Modelling Framework, the de-facto standard MDE (Model-Driven Engineering) framework making them freely available and ready-to-use for any software designer interested in using UCON for the definition of security policies in their new development projects.

In the context of Model-Driven Engineering applied to video games, software models are high-level abstractions that represent source code implementations of varied content such as the stages of the game, vehicles, or enemy entities (e.g., final bosses). In this work, we present our Evolutionary Model Generation (EMoGen) approach to generate software models that are comparable in quality to the models created by human developers. Our approach is based on an evolution (mutation and crossover) and assessment cycle to generate the software models. We evaluated the software models generated by EMoGen in the Kromaia video game, which is a commercial video game released on Steam and PlayStation 4. Each model generated byEMoGen has more than 1000 model elements. The results, which compare the software models generated by our approach and those generated by the developers, show that our approach achieves results that are comparable to the ones created manually by the developers in the retail and digital versions of the video game case study. However, our approach only takes five hours of unattended time in comparison to ten months of work by the developers. We perform a statistical analysis, and we make an implementation of EMoGen readily available.

Even though different applications based on Geographic Information Systems (GIS) provide different features and functions, they all share a set of common concepts (e.g., spatial data types, operations, services), a common architecture, and a common set of technologies. Furthermore, common structures appear repeatedly in different GIS, although they have to be specialized in specific application domains. Multilevel modeling is an approach to model-driven engineering (MDE) in which the number of metamodel levels is not fixed. This approach aims at solving the limitations of a two-level metamodeling approach, which forces the designer to include all the metamodel elements at the same level. In this paper, we address the application of multilevel modeling to the domain of GIS, and we evaluate its potential benefits. Although we do not present a complete set of models, we present four representative scenarios supported by example models. One of them is based on the standards defined by ISO TC/211 and the Open Geospatial Consortium. The other three are based on the EU INSPIRE Directive (territory administration, spatial networks, and facility management). These scenarios show that multilevel modeling can provide more benefits to GIS modeling than a two-level metamodeling approach.

A pesar del interés que despierta la tecnología blockchain y los contratos inteligentes, su complejidad y curva de aprendizaje supone un problema que ralentiza su adopción. Con la intención de contribuir a minimizar este problema, en la última edición de estas jornadas presentábamos una propuesta metodológica y tecnológica para el uso de modelos en el ámbito de los contratos inteligentes. El objetivo de este trabajo es presentar uno de los componentes tecnológicos que hemos desarrollado para implementar esta propuesta: un lenguaje de bloques para la especificación y representación gráfica de contratos inteligentes.

Model finders can build instances of declarative specifications that satisfy a set of correctness constraints. Some model finders ensure some degree of diversity among the instances they compute. Nevertheless, each model finder uses its own definition of diversity, that may or may not match designer intent. In this paper, we propose a procedure that enables designers to capture the desired notion of diversity they are looking for. Using a simple domain-specific language, they can specify what elements in the specification are relevant when comparing the differences between two instances. This information can then be used to make any model finder diversity-aware while using it as a black box. As a proof of concept, this approach has been implemented on top of the Alloy Analyzer.

It is becoming increasingly common to exploit the data collected by Information Systems in order to carry out an analysis of them and obtain conclusions that give rise to a series of decisions in the different research fields. The fact that in most cases these conclusions cannot be properly backed up has given rise to a reproducibility crisis in Data Science, the discipline that makes it possible to convert such data into knowledge, and in research fields that apply it.In this paper we envision a Model-Driven framework to foster reproducible and replicable Data Science projects. The framework proposes the definition of systematic pipelines that may be (semi)automatically executed in terms of concrete implementation platforms. Proprietary or third party tools are also considered so that flexibility may be ensured without hindering.

Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate frail elderly people in order to provide them with customized therapy plans. The whole process includes the completion of standardized questionnaires or specific movements, which are performed by the patient and do not necessarily require the presence of a medical expert. With the aim to automate CGA tests in as much as possible, we have designed and developed CLARC: a mobile robot aimed at helping physicians to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined tests. The design of CLARC has required dealing with both functional (robot’s skills and tasks) and non-functional aspects (e.g. performance, safety, or user satisfaction, among others). This paper describes a novel model-based approach aimed at helping designers (1) to specify the contextual information available to the robot; the Non-Functional Properties (NFP considered relevant for a given application; and how (and to what extent) changes in the context may affect these properties; and, from these models (2) to generate the runtime infrastructure allowing the robot to monitor its execution context and estimate high-level QoS metrics to know how well it is performing in terms of the selected NFPs.

Context: Maintenance activities cannot be completed without locating the set of software artifacts that realize a particular feature of a software system. Manual Feature Location (FL) is widely used in industry, but it becomes challenging (time-consuming and error prone) in large software repositories. To reduce manual efforts, automated FL techniques have been proposed. Research efforts in FL tend to make comparisons between automated FL techniques, ignoring manual FL techniques. Moreover, existing research puts the focus on code, neglecting other artifacts such as models.Objective: This paper aims to compare manual FL against automated FL in models to answer important questions about performance, productivity, and satisfaction of both treatments.Method: We run an experiment for comparing manual and automated FL on a set of 18 subjects (5 experts and 13 non-experts) in the domain of our industrial partner, BSH, manufacturer of induction hobs for more than 15 years. We measure performance (recall, precision, and F-measure), productivity (ratio between F-measure and spent time), and satisfaction (perceived ease of use, perceived usefulness, and intention to use) of both treatments, and perform statistical tests to assess whether the obtained differences are significant.Results: Regarding performance, manual FL significantly outperforms automated FL in precision and F-measure (up to 27.79+ACU and 19.05+ACU, respectively), whereas automated FL significantly outperforms manual FL in recall (up to 32.18+ACU). Regarding productivity, manual FL obtains 3.43+ACU-/min, which improves automated FL significantly. Finally, there are no significant differences in satisfaction for both treatments.Conclusions: The findings of our work can be leveraged to advance research to improve the results of manual and automated FL techniques. For instance, automated FL in industry faces issues such as low discrimination capacity. In addition, the obtained satisfaction results have implications for the usage and possible combination of manual, automated, and guided FL techniques.