ModelXGlue: a benchmarking framework for ML tools in MDE
José Antonio Hernández López,
Jesús Sánchez Cuadrado,
Riccardo Rubei
et al.
Abstract:The integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A … Show more
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