Work

A data-driven decision model for selecting ML models in research software

Year: 2026

Type: article

Abstract: Context: The process of selecting machine learning models is complex for research software engineers, requiring careful consideration of factors like trainability and comprehensibility to ensure long-term usability and success.Method: A meta-model was created to guide model selection, drawing from systematic literature reviews, expert interviews, case studies, and design science. Each phase contributed valuable insights and refined the decision-making framework.Results: The study analyzed 43 models across 72 attributes, resulting in a taxonomy of ML paradigms, approaches, and domains. Key findings include trends in model selection, combinations, evaluation metrics, and datasets. The decision model was further refined through expert feedback and validated with eight case studies.Contribution: This data-driven decision model supports research software engineers in selecting optimal ML models for integration into research software.Continued development is recommended to enhance its accuracy and applicability across varied research scenarios.

Source: Journal of Systems and Software

DOI: 10.1016/j.jss.2026.112821

Authors: Elena Baninemeh, Lex Steffens, Slinger Jansen, Siamak Farshidi

Institutions: Utrecht University, Lappeenranta-Lahti University of Technology, Wageningen University & Research

Language: English

Topic: Software Engineering Techniques and Practices

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,820

Funder: Universiteit Utrecht