A domain-specific language for model mutation and its application to the automated generation of exercises. (September 2017)

• Record Type: Journal Article
• Authors: Gómez-Abajo, Pablo; Guerra, Esther; Lara, Juan de
• Is Part Of: Computer languages, systems & structures. Volume 49(2017)
• Page Start: 152
• Page End: 173
• Publication Date: 2017-09
• Subjects: Model-Driven Engineering -- Domain-Specific Languages -- Model mutation -- Education -- Automatic exercise generation and correction; Programming languages (Electronic computers) -- Periodicals; Computer networks -- Periodicals; Computer architecture -- Periodicals; Computer systems -- Periodicals; Langage de programmation; Réseau d'ordinateurs; Architecture d'ordinateur; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 005.13
• DOI: 10.1016/j.cl.2016.11.001
• Languages: English
• ISSNs: 1477-8424

Abstract

Abstract: Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as main assets in all development phases. While many languages for model manipulation exist (e.g., for model transformation or code generation), there is a lack of frameworks to define and apply model mutations. A model mutant is a variation of an original model, created by the application of specific model mutation operations. Model mutation has many applications, for instance, in the areas of model transformation testing, model-based testing or education. In this paper, we present a domain-specific language calledWodel for the specification and generation of model mutants.Wodel is domain-independent, as it can be used to generate mutants of models conformant to arbitrary meta-models. Its development environment is extensible, permitting the incorporation of post-processors for different applications. In particular, we describeWodel-Edu, a post-processing extension directed to the automated generation of exercises for particular domains and their automated correction. We show the application ofWodel-Edu to the generation of exercises for deterministic automata, and report on an evaluation of the quality of the generated exercises, obtaining overall good results. Abstract : Highlights: We propose the languageWodel for meta-model independent model mutation. Wodel is extensible with post-processors for different applications. We show an application for the automatic generation of self-assessment exercises. A preliminary evaluation of the exercises quality shows good results.