  When working in large and complex codebases, developers face challenges using
\textit{Find Usages} to understand how to reuse classes and methods. To better
understand these challenges, we conducted a small exploratory study with 4
participants. We found that developers often wasted time reading long lists of
similar usages or prematurely focused on a single usage. Based on these
findings, we hypothesized that clustering usages by the similarity of their
surrounding context might enable developers to more rapidly understand how to
use a function. To explore this idea, we designed and implemented \textit{Find
Unique Usages}, which extracts usages, computes a diff between pairs of usages,
generates similarity scores, and uses these scores to form usage clusters. To
evaluate this approach, we conducted a controlled experiment with 12
participants. We found that developers with Find Unique Usages were
significantly faster, completing their task in 35% less time.
