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Classification and Challenges of Non-Functional Requirements in ML-Enabled Systems: A Systematic Literature Review

Version 9 2025-02-10, 15:13
Version 8 2024-12-26, 15:47
Version 7 2024-09-25, 11:09
Version 6 2024-07-30, 15:49
Version 5 2024-05-04, 15:10
Version 4 2024-04-10, 07:18
Version 3 2023-11-28, 09:11
Version 2 2023-07-27, 10:54
Version 1 2023-05-13, 10:21
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posted on 2025-02-10, 15:13 authored by Vincenzo De MartinoVincenzo De Martino, Fabio Palomba
Context: Machine learning (ML) is nowadays so pervasive and diffused that virtually no application can avoid its use. Nonetheless, its enormous potential is often tempered by the need to manage non-functional requirements (NFRs) and navigate pressing, contrasting trade-offs.
Objective: In this respect, we notice a lack of systematic synthesis of challenges explicitly tied to achieving and managing NFRs in ML-enabled systems. Such a synthesis may not only provide a comprehensive summary of the state of the art but also drive further research on the analysis, management, and optimization of NFRs of ML-enabled systems.
Method: In this paper, we propose a systematic literature review targeting two key aspects such as (1) the classification of the NFRs investigated so far, and (2) the challenges associated with achieving and managing NFRs in ML-enabled systems during model development
Through the combination of well-established guidelines for conducting systematic literature reviews and additional search criteria, we survey a total amount of 130 research articles.
Results: Our findings report that current research identified 31 different NFRs, which can be grouped into six main classes.
We also compiled a catalog of 26 software engineering challenges, emphasizing the need for further research to systematically address, prioritize, and balance NFRs in ML-enabled systems.
Conclusion: We conclude our work by distilling implications and a future outlook on the topic.

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