7 citations · 13 across the 5 of their papers we have counts for
5 papers
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…
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…
A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools
Kelly Azevedo, Luigi Quaranta, Fabio Calefato +1
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these…
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…