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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems

Lidia López, Claudia Ayala, Cristina Gómez, M. López
2 citations
SoftwareX
Model-Driven Software Engineering Techniques
4.62
article

MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems

Lidia López, Claudia Ayala, Cristina Gómez, M. López
article en
2 citations

Abstract

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.

SoftwareXVol. 32
Universitat Politècnica de Catalunya (ES)
Openalex Percentile: Top 5%
Model-Driven Software Engineering Techniques
4.62
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.