An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today's data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems...

Celý popis

Podrobná bibliografie

Vydáno v:	Proceedings / International Conference on Software Engineering s. 238 - 250
Hlavní autoři:	Tang, Yiming, Khatchadourian, Raffi, Bagherzadeh, Mehdi, Singh, Rhia, Stewart, Ajani, Raja, Anita
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.05.2021
Témata:	Best practices Complex systems Data mining Deep learning empirical studies machine learning systems Open source software refactoring Software engineering software evolution software repository mining technical debt
ISBN:	1665402962, 9781665402965
ISSN:	1558-1225
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!