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  • We conducted a Systematic Mapping Study following Kitchenham's guidelines.

    Our aim is to map-out the SPL evolution area, providing an overview on the field.

    The overall research questions we aim to answer are the following:

    RQ1: What types of research have been reported, to what extent, and how is coverage evolving?

    RQ2: Which product-derivation approach received most coverage, and how is coverage evolving?

    RQ3: Which kind of SPL asset received more attention, and how is attention evolving?

    RQ4: Which activities of the evolution life-cycle received most coverage, and how is this coverage evolving?

  • ScheMol is a Domain Specific Language (DSL) tailored for extracting models out of a database. ScheMol is a joint work between ModelUm Research Group (University of Murcia) and Onekin Research Group (University of the Basque Country).
    Oscar Díaz1, Gorka Puente1, Javier Luis Cánovas Izquierdo2 and Jesús García Molina2

    ONEKIN Research Group1, University of the Basque Country and ModelUM Research Group2, University of Murcia e-mail:
    {oscar.diaz,gorka.puente}@ehu.es1 {jlcanovas, jmolina}@um.es2

    Data rather than functionality, is the source of competitive advantage for Web2.0 applications such as wikis, blog and tagging sites. This valuable information might need to be capitalized by third-party applications or be subject to migration or data analysis. Model-Driven Engineering (MDE) can be used for these purposes. However, this first requires obtaining models from the wiki/blog/tagging site database (a.k.a. model harvesting). This can be achieved through SQL scripts embedded into the code. However, this approach leads to laborious code that exposes the iterations and table joins that serve to build the model. By contrast, a Domain Specific Language (DSL) can hide these “how” concerns, leaving the designer to focus on the “what”: the mapping of database schemas to model classes. This paper introduces Schemol, a DSL tailored for extracting models out of databases which considers Web2.0 specifics. Web2.0 applications are often built on top of general frameworks (a.k.a. engines) that set the database schema (e.g., MediaWiki, Blojsom). Hence, table names offer little help in automating the extraction process. Additionally, Web2.0 data tends to be annotated. User-provided data (e.g., wiki articles, blog entries) might contain semantic markups which provide helpful hints for model extraction. Unfortunately, this data ends up being stored as opaque strings. Therefore, there exists a considerable conceptual gap between the source database and the target metamodel. Schemol offers extractive functions and view-like mechanisms to confront these issues. Examples using Blojsom as the blog engine are available for download.

  • HandyMOF is a tool explained in the paper "Testing MOFScript transformations with HandyMOF"

  • GitLine is a Firefox Add-On (working on 37.0 version), which offers extra functionality on top of GitHub. GitLine aids SPL product builders to create and sync assets between product builders repositories (a.k.a Product Repositories) and the assets builders' repository (a.k.a Core Assets Repository). Operations for product builders are:

    -Product Fork: creates a new GitHub Repository from a Core Asset Repository
    -Update Propagation: update assets in Product Repositories with the latest versions from the Core Asset Repository.
    -Feedback Propagation: propose a customization in Product Repository to be promoted as a core asset in the Core Asset Repository

  • Mind maps are reckoned to be a valuable, visual approach for people to collaborate and share ideas, where new and improved ideas may be raised by association. However, there are not mechanisms to harness the potential of online communities to help users during mind mapping. To this end, it is presented a system that assists with suggestions during mind mapping, providing reactive and proactive suggestions. The former are received by users as response to their inputs in mind maps and retrieved from DBpedia. The latter are asked by the user to her Twitter community and received as tweets. A proactive suggestion is linked to its tweet and a reactive suggestion to its DBpedia entry, enabling seamless navigation through the context. In both cases, the community become the essential part of the system. This system is implemented as a FreeMind plugin.

ONEKIN, UNIVERSITY OF THE BASQUE COUNTRY

University of the basque country