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A decision-making support system for Enterprise Architecture Modelling

Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods have been proposed to deal with drawbacks of manual modelling, such as error-proneness, time-consumption, slow and poor re-adaptation, and cost. However, automatic modelling is not effective for the most abstract concepts in EA like strategy or motivational aspects. Thus, companies are demanding hybrid approaches that combines automatic with manual modelling. In this context there are no clear relationships between the input artefacts (and mining techniques) and the target EA viewpoints to be automatically modelled, as well as relationships between the experts’ roles and the viewpoints to which they might contribute in manual modelling. Consequently, companies cannot make informed decisions regarding expert assignments in EA modelling projects, nor can they choose appropriate mining techniques and their respective input artefacts. This research proposes a decision support system whose core is a genetic algorithm. The proposal first establishes (based on a previous literature review) the mentioned missing relationships and EA model specifications. Such information is then employed using a genetic algorithm to decide about automatic, manual or hybrid modelling by selecting the most appropriate input artefacts, mining techniques and experts. The genetic algorithm has been optimized so that the system aids EA architects to maximize the accurateness and completeness of EA models while cost (derived from expert assignments and unnecessary automatic generations) are kept under control.

Autores: Ricardo Pérez-Castillo / Francisco Ruiz / Mario Piattini / 
Palabras Clave: ArchiMate - Enterprise Architecture - Genetic algorithm - reverse engineering - Viewpoint

Towards a model-driven engineering solution for language independent mutation testing

Mutation testing is a technique to assess test suite adequacy to distinguish between correct and incorrect programs. Mutation testing applies one or more small changes to a program to obtain variants called mutants. The adequacy of a test suite is measured by determining how many of the mutants it distinguishes from the original program. There are many works about mutation testing, but the existing approaches focus on a specific programming language, and usually, it is not easy to customize the set of mutation operators. In this paper, we present Wodel-Test, an extension of the Wodel tool that implements a language-independent mutation testing framework based on model-driven engineering principles.

Autores: Pablo Gómez-Abajo / Esther Guerra / Juan de Lara / Mercedes G. Merayo / 
Palabras Clave: Domain Specific Languages - model mutation - Model-Driven Engineering - Mutation testing - reverse engineering

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