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

Approved

Classifications

MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal

Authors of the publication

Number of authors
7
Authors
Recupito, Gilberto; Pecorelli, Fabiano; Catolino, Gemma; Lenarduzzi, Valentina; Taibi, Davide; Di Nucci, Dario; Palomba, Fabio

Publication channel information

Title of journal/series
Journal of systems and software
ISSN (print)
0164-1212
ISSN (electronic)
1873-1228
ISSN (linking)
0164-1212
Publisher
Elsevier
Publication forum ID
61771
Publication forum level
3
Country of publication
United States
Internationality
Yes

Detailed publication information

Publication year
2024
Bibliographical publication year
2024
Reporting year
2024
Journal/series volume number
216
Article number
112151
DOI
10.1016/j.jss.2024.112151
Language of publication
English

Co-publication information

International co-publication
Yes
Co-publication with a company
No

Availability

Link to self-archived version
https://urn.fi/URN:NBN:fi:oulu-202408135355

Classification and additional information

MinEdu field of science classification
213 Electronic, automation and communications engineering, electronics, 113 Computer and information sciences
Keywords
AI technical debt; Empirical software engineering; Software engineering for artificial intelligence; Software quality; Survey studies

Funding information

Funding information in the publication
This work has been partially supported by the QualAI national research project, which have been funded by the MUR under the PRIN 2022 program (Contracts 2022B3BP5S).

Research data information

Research data information in the publication
This paper includes data as electronic supplementary material. Datasets generated and analyzed in the context of this study, raw results, and detailed plots, as well as additional resources useful for reproducing our research, are available in the online appendix of this paper: https://doi.org/10.6084/m9.figshare.24030456
Identifiers
Dataset identifier
10.6084/m9.figshare.24030456

Source database ID

WoS ID
WOS:001270607300001
Scopus ID
2-s2.0-85198063977