2 citations · 5 across the 5 of their papers we have counts for
6 papers
Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems
Rodrigo Ximenes, Antonio Pedro Santos Alves, Tatiana Escovedo +2
[Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks t…
Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil
Antonio Pedro Santos Alves, Marcos Kalinowski, Daniel Mendez +4
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unreal…
Naming the Pain in Machine Learning-Enabled Systems Engineering
Marcos Kalinowski, Daniel Mendez, Görkem Giray +12
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to…
Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding
Raphael Cabral, Marcos Kalinowski, Maria Teresa Baldassarre +3
[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly h…
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems
Eduardo Zimelewicz, Marcos Kalinowski, Daniel Mendez +13
[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are en…
Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey
Antonio Pedro Santos Alves, Marcos Kalinowski, Görkem Giray +12
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (R…