Universitat Politècnica de Catalunya
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació
Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering
2025-12
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.
This work was supported by the “Spanish Ministerio de Ciencia e Innovación” under project / funding scheme PID2024-156019OB-I00.
Peer Reviewed
Postprint (published version)
Article
English
Àrees temàtiques de la UPC::Informàtica::Enginyeria del software; Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic; Machine learning pipeline; Software quality; Low-code; Code generation; Python
https://www.sciencedirect.com/science/article/pii/S2352711025003450
info:eu-repo/grantAgreement/AEI/PLAN ESTATAL DE INVESTIGACIÓN CIENTÍFICA Y TÉCNICA Y DE INNOVACIÓN 2024-2027/PID2024-156019OB-I00/Continuous and Efficient Evolution of ML Systems: an Ecosystem-driven Approach
http://creativecommons.org/licenses/by/4.0/
Open Access
Attribution 4.0 International
E-prints [73034]
