  Generative models have become adept at producing artifacts such as images,
videos, and prose at human-like levels of proficiency. New generative
techniques, such as unsupervised neural machine translation (NMT), have
recently been applied to the task of generating source code, translating it
from one programming language to another. The artifacts produced in this way
may contain imperfections, such as compilation or logical errors. We examine
the extent to which software engineers would tolerate such imperfections and
explore ways to aid the detection and correction of those errors. Using a
design scenario approach, we interviewed 11 software engineers to understand
their reactions to the use of an NMT model in the context of application
modernization, focusing on the task of translating source code from one
language to another. Our three-stage scenario sparked discussions about the
utility and desirability of working with an imperfect AI system, how acceptance
of that system's outputs would be established, and future opportunities for
generative AI in application modernization. Our study highlights how UI
features such as confidence highlighting and alternate translations help
software engineers work with and better understand generative NMT models.
