Evaluating pedagogical practices supporting collaborative learning for model-based system development courses

Model-based software development (MBSD) has been widely used in industry for its effectiveness of code generation, code reuse and system evolution. At different stages of the software lifecycle, models -- as opposed to actual code -- are used as abstractions to present software development artifacts. In a university software engineering curriculum, compared to other concrete and tangible courses, e.g., game and app development, these levels of abstraction are often difficult for students to understand, and further, to see models' usefulness in practice. This paper presents an evaluation of pedagogical practices supporting collaborative learning for MBSD courses from experiences of teaching them at University of Oslo. The focus is to answer two research questions: 1) What are the challenges and possibilities when using a collaborative learning approach for teaching modelling and architecture? 2) What are the challenges and benefits of having a holistic approach to MBSD courses in light of the requirements of academia and the needs of industry? The term “holistic” is understood 1) as an approach that involves human factors (users), technology and processes, 2) as an approach to teaching MBSD courses where modelling for Enterprise Architecture is taught together with System Architecture and Model-Driven Language Engineering. Empirical data was collected through interviews, questionnaires, and document analysis. The paper’s research results show that three different course perspectives (Modeling for Enterprise Architecture with Business Architecture, System Architecture and Model Driven Language Engineering) are essential parts of teaching modeling courses, and an industry field study shows that industry sees the potential of having junior architects to provide support to a team and solving basic architectural problems

Un Framework para la generación automática de ejercicios mediante técnicas de mutación

En este trabajo se describe el diseño e implementación de un entorno que genera ejercicios tipo test de forma automática mediante técnicas de mutación, llamado Wodel-Edu. Wodel-Edu es una extensión de post-procesado para el Lenguaje de Dominio Específico (DSL) Wodel, desarrollado por el grupo MISO, y que proporciona primitivas de alto nivel para mutación de modelos. Para ello, se ha extendido el DSL Wodel con nuevas primitivas de mutación, nuevas estrategias de selección, un registro de las mutaciones aplicadas, un control de la generación de mutantes duplicados, y una comprobación de que los mutantes que se generan son modelos correctos (conformes a su meta-modelo). También se ha dotado a Wodel de un mecanismo extensible que permite registrar distintas acciones de post-procesado sobre los mutantes generados, extensión sobre la que se ha implementado el entorno Wodel-Edu. Wodel-Edu es independiente del dominio, y genera tres formatos diferentes de ejercicios tipo test: el primero, en el que se presentan varios diagramas, y el estudiante ha de decidir cuál es el correcto; el segundo, en el que se presenta un único diagrama, y el estudiante ha de decidir si es correcto, o no; el tercer formato, se presentan una serie de posibles cambios a realizar sobre el diagrama para corregirlo, y el estudiante ha de seleccionar cuáles de estos cambios son correctos. En este trabajo se ha elegido utilizar Wodel-Edu para generar ejercicios de autómatas finitos. Se presenta además una evaluación de la aplicación de ejercicios generada.This work presents the design and development of a framework for the automatic generation of test exercises using mutation techniques, that we call Wodel-Edu. Wodel-Edu is a post-processing extension for the Domain-Specific Language (DSL) Wodel - developed by MISO group - that provides high level primitives for model mutation. We extend the DSL Wodel with new mutation primitives, new selection strategies, a registry of the applied mutations, a duplicated mutant generation control, and a verification that the generated mutants are conforming to the meta-model. We also improve Wodel with an extensible mechanism that allows applying post-processing actions to the generated mutants, and we use this feature to include the Wodel-Edu extension in the Wodel environment. Wodel-Edu is domain independent, and generates three kind of test exercises: the first one, where several diagrams are shown to the student, and he has to choose which one is correct; the second one, where just one diagram is shown to the student, and he has to choose if it is correct or not; and the third kind of exercise, where several changes, that can be applied to the diagram, are presented to the student, and he has to choose which of these changes are correct. In this work, we chose to apply Wodel-Edu to generate finite automata exercises. We also present an evaluation of the generated test application