MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems
Machine learning-based systems play a critical and increasingly pervasive role in various aspects of daily life. Despite the growing recognition of the importance of producing high-quality code for Machine Learning (ML) pipelines to ensure proper evolution, maintenance, and reusability, actionable guidance at the design and implementation levels remains scarce. This paper introduces MLSToolbox Code Generator, a low-code tool designed to support data scientists in graphically defining ML pipelines and generating their corresponding Python code. The tool leverages core Software Engineering design principles to promote high-quality Python code. Through a detailed example, we demonstrate how data scientists can use the tool. The flexible and extensible architecture of the tool enables data scientists to customize ML pipeline generation to meet domain-specific requirements, fostering greater efficiency and adaptability in ML workflows.
Authors
- Lidia López (ORCID: https://orcid.org/0000-0002-6901-9223)
- Claudia Ayala (ORCID: https://orcid.org/0000-0002-6262-3698)
- Cristina Gómez (ORCID: https://orcid.org/0000-0002-3872-0439)
- M. López (ORCID: https://orcid.org/0009-0003-9973-1128)
Institutions
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- SoftwareX
- Published
- 2025-10-07
- DOI
- https://doi.org/10.1016/j.softx.2025.102379
- Citations
- 2
- Primary Topic
- Model-Driven Software Engineering Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.62