A DSL and model transformations to specify learning corpora for modeling assistants

Abstract

Software engineering undergraduate students spend a significant time learning various topics related to software design, including notably model-driven engineering (MDE), where different types of structural and behavioral models are used to design, implement, and validate an application. MDE instructors spend a lot of time covering modeling concepts, which is more difficult with ever-increasing class sizes. Online resources, such as learning corpora for domain modeling, can aid in this learning process by serving as a more dynamic textbook alternative or as part of a larger interactive application with domain modeling exercises and tutorials. A Learning Corpus (LC) is an extensible list of entries representing possible mistakes that could occur when defining a model, e.g., Missing Abstraction-Occurrence pattern in the case of a domain model. Each LC entry includes progressive levels of feedback, including written responses, quizzes, and references to external resources. To make it easy for instructors to customize the entries as well as add their own, we propose a novel, simple, and intuitive approach based on an internal domain-specific language that supports features such as context-specific information and concise arbitrary metamodel navigation with shorthands. Transformations to source code as well as Markdown and LATEX enable use of the LC entries in different contexts. These transformations as well as the integration of the generated code in a sample Modeling Assistant application verify and validate the LC metamodel and specification.