2026-04-14T02:14:10Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4466072026-01-21T07:30:28Zcom_2072_1033col_2072_452950

MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems Gómez Seoane, Cristina López Cuesta, Lidia Ayala Martínez, Claudia Patricia Lopez Cuesta, Miguel 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 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 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) 2025-12 Article Gomez, C. [et al.]. MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems. «SoftwareX», Desembre 2025, vol. 32, article 102379. 2352-7110 https://hdl.handle.net/2117/446607 10.1016/j.softx.2025.102379 eng 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 10 p. application/pdf