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.Proyectos TIN2011-23795, TIN2011-15497-E y Andalucía Tech Campus de Excelencia

On the Modular Specification of NFPs: A Case Study

The modular specification of non-functional properties of systems is a current challenge of Software Engineering, for which no clear solution exists. However, in the case of Domain-Specific Languages some successful proposals are starting to emerge, combining model-driven techniques with aspect-weaving mechanisms. In this paper we show one of these approaches in practice, and present the implementation we have developed to fully support it. We apply our approach for the specification and monitoring of non-functional properties using observers to a case study, illustrating how generic observers defining non-functional properties can be defined in an independent manner. Then, correspondences between these observers and the domain-specific model of the system can be established, and then weaved into a unified system specification using ATL model transformation. Such a unified specification can also be analyzed in a natural way to obtain the required non-functional properties of the system.This work is partially funded by Research Projects TIN2011-23795 and TIN2011-15497-E

A Domain Specific Visual Language for Modeling Power-Aware Reliability in Wireless Sensor Networks

Reliability is an attribute that appears in all quality models, so it is important to take it into account when developing any kind of system. Its evaluation at latter stages of the software development may force the re-engineering of im-portant parts of the system, something very costly. This is why it should be raised to the system design phase. Among the systems where reliability is a crucial issue, some wireless sensor network (WSN) protocols aim to extend the networks lifetime as much as possible, so a more reliable network will live longer. Following a model-driven engineering (MDE) approach, we propose the use of domain speci c visual lan- guages (DSVLs) to model the reliability of systems based on components by means of in-place behavioral rules and by modeling how the state of the components changes. We have developed as well a DSVL for modeling and analyzing reliability properties of a WSN protocol based on local in- formation, namely directional source-aware routing protocol (DSAP).Ministerio de Ciencia e Innovación TIN2011-2379

A Rewriting Logic Semantics for ATL

As the complexity of model transformation (MT) grows, the need to rely on formal semantics of MT languages becomes a critical issue. Formal semantics provide precise speci cations of the expected behavior of transformations, allowing users to understand them and to use them properly, and MT tool builders to develop correct MT engines, compilers, etc. In addition, formal semantics allow modelers to reason about the MTs and to prove their correctness, something specially important in case of large and complex MTs (with, e.g., hundreds or thousands of rules) for which manual debugging is no longer possible. In this paper we give a formal semantics of the ATL 3.0 model transformation language using rewriting logic and Maude, which allows addressing these issues. Such formalization provides additional bene ts, such as enabling the simulation of the speci cations or giving access to the Maude toolkit to reason about them

Specification and simulation of queuing network models using Domain-Specific Languages

Queuing Network Models (QNMs) provide powerful notations and tools for modeling and analyzing the performance of many different kinds of systems. Although several powerful tools currently exist for solving QNMs, some of these tools define their own model representations, have been developed in platform-specific ways, and are normally difficult to extend for coping with new system properties, probability distributions or system behaviors. This paper shows how Domain Specific Languages (DSLs), when used in conjunction with Model-driven engineering techniques, provide a high-level and very flexible approach for the specification and analysis of QNMs. We build on top of an existing metamodel for QNMs (PMIF) to de ne a DSL and its associated tools (editor and simulation engine), able to provide a high-level notation for the specification of different kinds of QNMs, and easy to extend for dealing with other probability distributions or system properties, such as system reliability.Ministerio de Ciencia e Innovación TIN2011-2379

Las modificaciones estructurales en el Anteproyecto de Ley del Código Mercantil

En este trabajo realizamos una aproximación a las novedades que contiene la regulación de las modificaciones estructurales en el Anteproyecto de Ley del Código Mercantil. La inclusión de esta materia en el futuro Código constituye un “trasvase normativo” de la Ley sobre modificaciones estructurales de las sociedades mercantiles, aunque, sin perjuicio de ello, también se incluyen novedades de relieve y numerosas mejoras de perfeccionamiento normativo. En este trabajo efectuamos un primer estudio de los cambios proyectados, al tiempo que apuntamos la conveniencia de reflexionar sobre otros aspectos susceptibles de mejora que no han sido considerados por el momento.Trabajo enmarcado en el Proyecto de investigación Ref. DER2012-37406, financiado por el Miniserio de Economía y Competitividad

Introducing Approximate Model Transformations

Model transformations dealing with very large models need to count on mechanisms and tools to be able to manage them. The usual approach to improve performance in these cases has focused on the use of concurrency and parallelization techniques, which aim at producing the correct output model(s). In this paper we present our initial approach to produce target models that are accurate enough to provide meaningful and useful results, in an efficient way, but without having to be fully correct. We introduce the concept of Approximate Model Transformations.Ministerio de Ciencia e Innovación TIN2011-23795European Commission ICT Policy Support Programme 31785

Towards Self-Adaptive Software for Wildfire Monitoring with Unmanned Air Vehicles.

Wildfires have evolved significantly over the last decades, burning increasingly large forest areas every year. Smart cyber-physical systems like small Unmanned Air Vehicles (UAVs) can help to monitor, predict, and mitigate wildfires. In this paper, we present an approach to build control software for UAVs that allows autonomous monitoring of wildfires. Our proposal is underpinned by an ensemble of artificial intelligence techniques that include: (i) Recurrent Neural Networks (RNNs) to make local UAV predictions about how the fire will spread over its surrounding area; and (ii) Deep Reinforcement Learning (DRL) to learn policies that will optimize the operation of the UAV team.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186