Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture-Reference-Cited by-同舟云学术

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Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture

Published:2024-10 Issue: Volume:216 Page:112151
ISSN:0164-1212
Container-title:Journal of Systems and Software
language:en
Short-container-title:Journal of Systems and Software

Author:

Recupito Gilberto,Pecorelli Fabiano,Catolino Gemma,Lenarduzzi Valentina,Taibi Davide^ORCID,Di Nucci Dario^ORCID,Palomba Fabio

Publisher

Elsevier BV

Reference66 articles.

1. The Assessment List for Trustworthy Artificial Intelligence (ALTAI);Ala-Pietilä,2020

2. Empirical analysis of hidden technical debt patterns in machine learning software;Alahdab,2019

3. Identification and management of technical debt: A systematic mapping study;Alves;Inf. Softw. Technol.,2016

4. Software engineering for machine learning: A case study;Amershi,2019

5. Experimental vignette designs for factorial surveys;Atzmüller;Kolner Z. Soziol. Sozialpsychol.,2006

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