Empowering OCL research: a large-scale corpus of open-source data from GitHub

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.