In model-driven software engineering, models are used in all phases of the development process. These models may get broken due to various editions during the modeling process. There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models, however, most of these tools do not prioritize customization to follow user requirements nor allow the extension of their components to adapt to different model types. In this paper, we present an extensible model repair framework which enable users to deal with different types of models and to add their own repair preferences to customize the results. The framework uses customizable learning algorithms to automatically find the best sequence of actions for repairing a broken model according to the user preferences. As an example, we customize the framework by including as a preference a model distance metric, which allows the user to choose a more or less conservative repair. Then, we evaluate how this preference extension affects the results of the repair by comparing different distance metric calculations. Our experiment proves that extending the framework makes it more precise and produces models with better quality characteristics.
Wed 21 OctDisplayed time zone: Eastern Time (US & Canada) change
13:15 - 14:30 | |||
13:15 20mFull-paper | To build, or not to build: ModelFlow, a build solution for MDE projectsFT Technical Track Beatriz Sanchez University of York, Dimitris Kolovos University of York, Richard Paige McMaster University Pre-print Media Attached | ||
13:35 20mFull-paper | An extensible framework for customizable model repairFT Technical Track Angela Barriga , Rogardt Heldal , Ludovico Iovino Gran Sasso Science Institute, L'Aquila, Italy, Magnus Marthinsen , Adrian Rutle Western Norway University of Applied Sciences | ||
13:55 20mFull-paper | Interactive Metamodel/Model Co-Evolution: A Clustering-based Multi-Objective ApproachFT Technical Track | ||
14:15 15mTalk | Ark: a constraint-based method for architectural synthesis of smart systemsJ1st Technical Track Milena Guessi , Flavio Oquendo IRISA (UMR CNRS) - Univ. Bretagne-Sud (UBS), Elisa Yumi Nakagawa University of São Paulo, Brazil DOI |