Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Villamizar, Hugo"'
Autor:
Alves, Antonio Pedro Santos, Kalinowski, Marcos, Mendez, Daniel, Villamizar, Hugo, Azevedo, Kelly, Escovedo, Tatiana, Lopes, Helio
[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 unrealistic customer expectations and vagueness in ML problem specifications
Externí odkaz:
http://arxiv.org/abs/2407.18977
Autor:
Kalinowski, Marcos, Mendez, Daniel, Giray, Görkem, Alves, Antonio Pedro Santos, Azevedo, Kelly, Escovedo, Tatiana, Villamizar, Hugo, Lopes, Helio, Baldassarre, Teresa, Wagner, Stefan, Biffl, Stefan, Musil, Jürgen, Felderer, Michael, Lavesson, Niklas, Gorschek, Tony
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 deliver a comprehensive overview of the current status quo of enginee
Externí odkaz:
http://arxiv.org/abs/2406.04359
Autor:
Cabral, Raphael, Kalinowski, Marcos, Baldassarre, Maria Teresa, Villamizar, Hugo, Escovedo, Tatiana, Lopes, Hélio
[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experi
Externí odkaz:
http://arxiv.org/abs/2402.05337
Autor:
Zimelewicz, Eduardo, Kalinowski, Marcos, Mendez, Daniel, Giray, Görkem, Alves, Antonio Pedro Santos, Lavesson, Niklas, Azevedo, Kelly, Villamizar, Hugo, Escovedo, Tatiana, Lopes, Helio, Biffl, Stefan, Musil, Juergen, Felderer, Michael, Wagner, Stefan, Baldassarre, Teresa, Gorschek, Tony
[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited, especially for activities surro
Externí odkaz:
http://arxiv.org/abs/2402.05333
In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challen
Externí odkaz:
http://arxiv.org/abs/2402.05334
Autor:
Alves, Antonio Pedro Santos, Kalinowski, Marcos, Giray, Görkem, Mendez, Daniel, Lavesson, Niklas, Azevedo, Kelly, Villamizar, Hugo, Escovedo, Tatiana, Lopes, Helio, Biffl, Stefan, Musil, Jürgen, Felderer, Michael, Wagner, Stefan, Baldassarre, Teresa, Gorschek, Tony
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems.
Externí odkaz:
http://arxiv.org/abs/2310.06726
Engineering successful machine learning (ML)-enabled systems poses various challenges from both a theoretical and a practical side. Among those challenges are how to effectively address unrealistic expectations of ML capabilities from customers, mana
Externí odkaz:
http://arxiv.org/abs/2309.07980
Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into ML tasks, experimentin
Externí odkaz:
http://arxiv.org/abs/2206.09760
Requirements engineering (RE) activities for machine learning (ML) are not well-established and researched in the literature. Many issues and challenges exist when specifying, designing, and developing ML-enabled systems. Adding more focus on RE for
Externí odkaz:
http://arxiv.org/abs/2204.07662
Publikováno v:
In The Journal of Systems & Software July 2024 213