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Mutation is a systematic technique to create variants of a seed artefact by means of mutation operators. It has many applications in computer science, like software testing, automatic exercise generation and design space exploration. Typically, mutation frameworks are developed ad-hoc by implementing mutation operators and their application strategies from scratch, using general-purpose programming languages. However, this is costly and error-prone. To improve this situation, we propose Wodel : a domain-specific language and tool for model-based mutation that is independent of the domain meta-model. Wodel enables the rapid development and application of model mutations. It provides built-in advanced functionalities like automatic generation of seed models, and static and dynamic metrics of operator coverage and applicability. It offers extension points, e.g., to post-process mutants and describe domain-specific equivalence criteria. As an example, we illustrate the usage of Wodel for the mutation of security policies, and present an empirical evaluation of its expressiveness. [ABSTRACT FROM AUTHOR]

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 called Wodel 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 describe Wodel-Edu , a post-processing extension directed to the automated generation of exercises for particular domains and their automated correction. We show the application of Wodel-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 FROM AUTHOR]