  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.
