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  • 1.
    Addazi, Lorenzo
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Cicchetti, Antonio
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Di Rocco, Juri
    University of L’Aquila, Italy.
    Di Ruscio, Davide
    University of L’Aquila, Italy.
    Iovino, Ludovico
    University of L’Aquila, Italy.
    Pierantonio, Alfonso
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. University of L’Aquila, Italy.
    Semantic-based Model Matching with EMFCompare2016In: CEUR Workshop Proceedings (CEUR-WS.org), Saint Malo, France: CEUR-WS , 2016, Vol. 1706, p. 40-49Conference paper (Refereed)
  • 2.
    Bucaioni, Alessio
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Cicchetti, Antonio
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ciccozzi, Federico
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Pierantonio, Alfonso
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards Design-Space Exploration of Component Chains in Vehicle Software2016In: 42nd Euromicro Conference series on Software Engineering and Advanced Applications, Work In Progress (WiP) SEAA 2016 WiP, 2016Conference paper (Refereed)
    Abstract [en]

    The size, complexity and heterogeneity of vehicular software systems has been constantly increasing. As a result, there is a growing consensus on the need to leverage modelbased techniques for automating, thus taming, error-proneness of tedious engineering tasks. Our methodology employs a one-tomany model transformation for generating a set of implementation models from a single design model. Then, it evaluates the appropriateness of each generated model by means of modelbased timing analysis. In this ongoing work, we discuss an enhancement of our methodology where model-based timing analysis is extended for running on a single model with uncertainty.

  • 3.
    Bucaioni, Alessio
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Arcticus Syst, Järfälla, Sweden.
    Cicchetti, Antonio
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ciccozzi, Federico
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Pierantonio, Alfonso
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Univ Aquila, DISIM, Laquila, Italy.
    Handling Uncertainty in Automatically Generated Implementation Models in the Automotive Domain2016In: 42nd Euromicro Conference series on Software Engineering and Advanced Applications SEAA 2016, 2016, p. 173-180Conference paper (Refereed)
    Abstract [en]

    Models and model transformations, the two core constituents of Model-Driven Engineering, aid in software development by automating, thus taming, error-proneness of tedious engineering activities. In most cases, the result of these automated activities is an overwhelming amount of information. This is the case of one-to-many model transformations that, e.g. in designspace exploration, can potentially generate a massive amount of candidate models (i.e., solution space) from one single model. In our scenario, from one design model we generate a set of possible implementation models on which timing analysis is run. The aim is to find the best model from a timing perspective. However, multiple implementation models can have equally good analysis results. Therefore, the engineer is expected to investigate the solution space for making a final decision, using criteria which fall outside the analysis’ criteria themselves. Since candidate models can be many and very similar to each other, manually finding differences and commonalities is an impractical and errorprone task. In order to provide the engineer with an expressive representation of models’ commonalities and differences, we propose the use of modelling with uncertainty. We achieve this by elevating the solution space to a first-class status, adopting a compact notation capable of representing the solution space by means of a single model with uncertainty. Commonalities and differences are thus represented by means of uncertainty points for the engineer to easily grasp them and consistently make her decision without manually inspecting each model individually.

  • 4.
    Di Rocco, J.
    et al.
    University of L’Aquila, L’Aquila, Italy .
    Di Ruscio, D.
    University of L’Aquila, L’Aquila, Italy .
    Pierantonio, Alfonso
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. University of L’Aquila, L’Aquila, Italy .
    Cuadrado, J. S.
    Universidad Autónoma de Madrid, Madrid, Spain .
    De Lara, J.
    Universidad Autónoma de Madrid, Madrid, Spain .
    Guerra, E.
    Universidad Autónoma de Madrid, Madrid, Spain .
    Using ATL transformation services in the MDEForge collaborative modeling platform2016In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 9765, 2016, 2016, p. 70-78Conference paper (Refereed)
    Abstract [en]

    In the last years, the increasing complexity of Model-Driven Engineering (MDE) tools and techniques has led to higher demands in terms of computation, interoperability, and configuration management. Harnessing the software-as-a-service (SaaS) paradigm and shifting applications from local, mono-core implementations to cloud-based architectures is key to enhance scalability and flexibility. To this end, we propose MDEForge: an extensible, collaborative modeling platform that provides remote model management facilities and prevents the user from focussing on time-consuming, and less creative procedures. This demo paper illustrates the extensibility of MDEForge by integrating ATL services for the remote execution, automated testing, and static analysis of ATL transformations. The usefulness of their employment under the SaaS paradigm is demonstrated with a case-study showing a wide range of new application possibilities. 

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