# Classification, Challenges, and Automated Approaches to Handle Non-Functional Requirements in ML-Enabled Systems: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2311.17483 · HTML: https://mlsanity.com/paper/2311.17483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)

## Abstract (arXiv)

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 and navigate pressing, contrasting trade-offs. Objective: In this respect, we notice the lack of a comprehensive synthesis of the non-functional requirements affecting ML-enabled systems, other than the major challenges faced to deal with them. 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 non-functional requirements of ML-intensive systems. Method: In this paper, we propose a systematic literature review targeting two key aspects such as (1) the classification of the non-functional requirements investigated so far, and (2) the challenges to be faced when developing models in ML-enabled systems. Through the combination of well-established guidelines for conducting systematic literature reviews and additional search criteria, we survey a total amount of 69 research articles. Results: Our findings report that current research identified 30 different non-functional requirements, which can be grouped into six main classes. We also compiled a catalog of more than 23 software engineering challenges, based on which further research should consider the nonfunctional requirements of machine learning-enabled systems. Conclusion: We conclude our work by distilling implications and a future outlook on the topic.