  [Context] In Brazil, 41% of companies use machine learning (ML) to some
extent. However, several challenges have been reported when engineering
ML-enabled systems, including unrealistic customer expectations and vagueness
in ML problem specifications. Literature suggests that Requirements Engineering
(RE) practices and tools may help to alleviate these issues, yet there is
insufficient understanding of RE's practical application and its perception
among practitioners. [Goal] This study aims to investigate the application of
RE in developing ML-enabled systems in Brazil, creating an overview of current
practices, perceptions, and problems in the Brazilian industry. [Method] To
this end, we extracted and analyzed data from an international survey focused
on ML-enabled systems, concentrating specifically on responses from
practitioners based in Brazil. We analyzed RE-related answers gathered from 72
practitioners involved in data-driven projects. We conducted quantitative
statistical analyses on contemporary practices using bootstrapping with
confidence intervals and qualitative studies on the reported problems involving
open and axial coding procedures. [Results] Our findings highlight distinct RE
implementation aspects in Brazil's ML projects. For instance, (i) RE-related
tasks are predominantly conducted by data scientists; (ii) the most common
techniques for eliciting requirements are interviews and workshop meetings;
(iii) there is a prevalence of interactive notebooks in requirements
documentation; (iv) practitioners report problems that include a poor
understanding of the problem to solve and the business domain, low customer
engagement, and difficulties managing stakeholders expectations. [Conclusion]
These results provide an understanding of RE-related practices in the Brazilian
ML industry, helping to guide research toward improving the maturity of RE for
ML-enabled systems.
