Technical Debt in AI-Enabled Systems: On the Prevalence, Severity, Impact, and Management Strategies for Code and Architecture

33 Pages Posted: 10 Oct 2023 Last revised: 13 May 2024

See all articles by Gilberto Recupito

Gilberto Recupito

University of Salerno

Fabiano Pecorelli

affiliation not provided to SSRN

Gemma Catolino

Tilburg University

Valentina Lenarduzzi

University of Oulu

Davide Taibi

University of Oulu

Dario Di Nucci

University of Salerno

Fabio Palomba

University of Salerno

Abstract

Context: Artificial Intelligence (AI) is pervasive in several application domains, promising to be even more diffused in the next decades. Developing high-quality AI-enabled systems is urgently needed to avoid performance drifts, undesirable socially-relevant consequences, or a slow reaction to evolutionary changes. Recent work proposed AI technical debt, a potential liability for developing AI-enabled systems whose impact is limited to system qualities. While the problem of AI technical debt is gaining the attention of the software engineering community, scientific knowledge on the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers' awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Method: We developed a survey study featuring 53 AI developers to collect information on the practical prevalence, severity, impact, and the strategies practitioners apply to identify and mitigate AI-specific architectural and code debt. Results: The study's key findings reveal the multiple impacts that code and architectural debt may have on the quality of AI-enabled systems.Conclusion: We conclude the article by distilling lessons learned and insights for researchers.

Keywords: AI Technical Debt, software quality, Survey Studies, Software Engineering for Artificial Intelligence, Empirical Software Engineering

Suggested Citation

Recupito, Gilberto and Pecorelli, Fabiano and Catolino, Gemma and Lenarduzzi, Valentina and Taibi, Davide and Di Nucci, Dario and Palomba, Fabio, Technical Debt in AI-Enabled Systems: On the Prevalence, Severity, Impact, and Management Strategies for Code and Architecture. Available at SSRN: https://ssrn.com/abstract=4598448 or http://dx.doi.org/10.2139/ssrn.4598448

Gilberto Recupito (Contact Author)

University of Salerno ( email )

Via Giovanni Paolo II, 132
Fisciano, 84084
Italy

Fabiano Pecorelli

affiliation not provided to SSRN ( email )

No Address Available

Gemma Catolino

Tilburg University ( email )

Valentina Lenarduzzi

University of Oulu ( email )

Davide Taibi

University of Oulu ( email )

Dario Di Nucci

University of Salerno ( email )

Fabio Palomba

University of Salerno ( email )

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