  Evaluating the performance of machine learning models on diverse and
underrepresented subgroups is essential for ensuring fairness and reliability
in real-world applications. However, accurately assessing model performance
becomes challenging due to two main issues: (1) a scarcity of test data,
especially for small subgroups, and (2) possible distributional shifts in the
model's deployment setting, which may not align with the available test data.
In this work, we introduce 3S Testing, a deep generative modeling framework to
facilitate model evaluation by generating synthetic test sets for small
subgroups and simulating distributional shifts. Our experiments demonstrate
that 3S Testing outperforms traditional baselines -- including real test data
alone -- in estimating model performance on minority subgroups and under
plausible distributional shifts. In addition, 3S offers intervals around its
performance estimates, exhibiting superior coverage of the ground truth
compared to existing approaches. Overall, these results raise the question of
whether we need a paradigm shift away from limited real test data towards
synthetic test data.
