EMMM: A Unified Meta-Model for Tracking Machine Learning Experiments
Paper i proceeding, 2022

Traditional software engineering tools for managing assets—specifically, version control systems—are inadequate to manage the variety of asset types used in machine-learning model development experiments. Two possible paths to improve the management of machine learning assets include 1) Adopting dedicated machine-learning experiment management tools, which are gaining popularity for supporting concerns such as versioning, traceability, auditability, collaboration, and reproducibility; 2) Developing new and improved version control tools with support for domain-specific operations tailored to machine learning assets. As a contribution to improving asset management on both paths, this work presents Experiment Management Meta-Model (EMMM), a meta-model that unifies the conceptual structures and relationships extracted from systematically selected machine-learning experiment management tools. We explain the metamodel’s concepts and relationships and evaluate it using real experiment data. The proposed meta-model is based on the Eclipse Modeling Framework (EMF) with its meta-modeling language, Ecore, to encode model structures. Our meta-model can be used as a concrete blueprint for practitioners and researchers to improve existing tools and develop new tools with native support for machine-learning-specific assets and operations.

Machine learning experiments



Management tools


Samuel Idowu

Göteborgs universitet

Daniel Strüber

Radboud Universiteit

Göteborgs universitet

Thorsten Berger

Ruhr-Universität Bochum

Göteborgs universitet

Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022

9781665461528 (ISBN)

48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022
Gran Canaria, Spain,


Annan data- och informationsvetenskap





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