Abstract
Model-driven engineering (MDE) enables the rise in abstraction during development in software and system design. In particular, meta-models become a central artifact in the process, and are supported by various other artifacts such as editors and transformation. In order to define constraints, invariants, and queries on model-driven artifacts, a generic language has been developed: the Object Constraint Language (OCL). In literature, many studies into OCL have been performed on small collections of data, mostly originating from a single source (e.g., OMG standards). As such, generalization of results beyond the data studied is often mentioned as a threat to validity. Creation of a benchmark dataset has already been identified as a key enabler to address the generalization threat. To facilitate further empirical studies in the field of OCL, we present the first large-scale dataset of 103262 OCL expression, systematically extracted from 671 GitHub repositories. In particular, our dataset has extracted these expressions from various types of files (a.o. metamodels and model-to-text transformations). In this work we showcase a variety of different studies performed using our dataset, and describe several other types that could be performed. We extend previous work with data and experiments regarding OCL in model-to-text (mtl) transformations.
Highlights
Model driven engineering (MDE) is being used in industry to drive increase in productivity (Hutchinson et al 2011b)
In Noten et al (2017a) we have described the data collection process, presented the dataset of Object Constraint Language (OCL) expressions derived from the Ecore meta-models, replicated a study of Cadavid et al (2015) and evaluated the assumptions made by a previous work of Anastasakis et al (2008)
In this work we present a publicly available dataset of OCL expressions derived from GitHub
Summary
Model driven engineering (MDE) is being used in industry to drive increase in productivity (Hutchinson et al 2011b) One such driver is the use of domain specific languages (DSLs) to allow engineers to specify systems in terms relevant to their domain, rather than encoding them into general purpose concepts like those of UML. These DSLs are underpinned by metamodels (Cuadrado and Molina 2007), which express the concepts and structure of possible models (i.e., abstract syntax). As DSLs grow in complexity, the expressivity of meta-models alone is often not sufficient to accurately specify the domain (Richters and Gogolla 1998) To address this problem, more complex mechanisms have been proposed, such as the Object Constraint Language (OCL) (Warmer and Kleppe 2003). A need for a more diverse and thorough dataset has been recognized in the literature (Gogolla et al 2013; Gogolla and Cabot 2016)
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