Software Engineering for AI (SE4AI)

Description

This research area explores the synergies between Software Engineering and Artificial Intelligence (AI) systems, with special emphasis on Machine Learning-Based Systems (MLS). Our main aim is to leverage established principles, practices, and methodologies from Software and Data Engineering to effectively support the design, development, and lifecycle management of all components of MLS.

Team

Cristina Gómez

UPC

Alberto Abelló

UPC

Claudia Ayala

UPC

Xavier Franch

UPC

Lidia López

UPC

Silverio Martínez-Fernández

UPC

Carme Quer

UPC

Santiago del Rey

UPC - PhD

Joel Castaño

UPC - PhD

Alexandra González

UPC - PhD

Outcomes/Main Contributions

Research:

• Applying Software Engineering principles and knowledge to improve the design and development of Machine Learning Systems (MLS).
  • A tool to define and generate Python code for ML pipelines following Software Design Principles. Gómez, Cristina, et al. "MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems." SoftwareX 32 (2025): 102379.
  • Assessment of the application of Software Design Principles for the development of Machine Learning Pipelines, with the aim of improving ML pipelines quality. López, Lidia, Cristina Gómez, and Claudia Ayala. "Insights on the use of software design principles in machine learning pipelines." International Conference on Product-Focused Software Process Improvement. Cham: Springer Nature Switzerland, 2024.
  • Assessment of ML models trustworthiness. Manzano, M., Ayala, C.P., Gómez, C.: TrustML: A Python package for computing the trustworthiness of ML models. SoftwareX 26: 101740 (2024).
  • Supporting end-to-end schema integration from heterogeneous and semi-structured data sources for conducting comprehensive data analysis for MLS. Flores, Javier, et al. "Incremental schema integration for data wrangling via knowledge graphs." Semantic Web 15.3 (2024): 793-830.
• Applying Data Engineering techniques to improve the performance of pipelines and storage systems:
  • Storage management. E. Gallinucci, M. Golfarelli, W. Radwan, G. Zarate, A. Abelló: "Impact Study of NoSQL Refactoring in SkyServer Database". DOLAP 2025: 1-11.
  • Multi-objective feature selection. U. F. Njoku, A. Abelló, B. Bilalli, G. Bontempi. "Towards fair machine learning using many-objective feature selection". Appl. Soft Comput. 181: 113411 (2025)
  • Optimization of pipelines. A. Kontaxakis, D. Sacharidis, A. Simitsis, A. Abelló, S. Nadal: "HYPPO: Using Equivalences to Optimize Pipelines in Exploratory Machine Learning". ICDE 2024: 221-234.
• Software engineering artifacts (e.g., requirements, architecture, changes) beyond an ML component and its standard constituents:
  • State of the art on Software Engineering for AI-Based Systems: A Survey.
  • Research Directions for Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems.
  • Requirements engineering for AI.
  • Software Architecture and Software Design Decisions for AI.
  • Software changes on AI systems.
  • Testing on AI systems.
• Experiences on the development and operation of Machine Learning Software Systems as a whole, specifically in the following applications:
  • Chess broadcasting.
  • Mushroom classification.
  • Boosting ADHD productivity in a virtual reality environment.
  • Sport analytics and improving fitness.

Technology Transfer:

• Development of MLS-Toolbox, a set of tools to support ML pipeline development:
  • MLS-Toolbox on GitHub: Includes a low-code application for ML pipeline code generation where the user can define a pipeline graphically and generate Python code.
  • A preliminary version of a quality assessment tool to assess ML pipelines written in Python.
• DORM: A tool to facilitate schema evolution, by allowing to generate database schemas and queries in a flexible and dynamic way.
• TrustML: A Python package for computing the trustworthiness of ML models. This package supports evaluating ML models' trustworthiness both during their development process and in production environments.

Teaching:

• Teaching MLOps in Higher Education through Project-Based Learning:
  • Experiences from Training Future Machine Learning Engineers with Software Engineering Practices.
  • The state-of-the-art outcomes of this research area (and the sustainability of AI systems research area) are integrated into two subjects:
    • “Machine Learning Systems in Production” (MLOps) of the Master of Data Science at UPC.
    • “Advanced Topics of Data Engineering 2” (TAED2) of the Bachelor degree of Data Science and Engineering at UPC.

Community:

• Involvement in SE4AI conferences:
  • International Conference on AI Engineering – Software Engineering for AI (CAIN@ICSE).
  • International Workshop on Software Architecture and Machine Learning (SAML).

Projects Overview

Collaborations

Contact

Cristina Gómez Contact