  Context: Roblox Studio lets millions of creators build interactive
experiences by programming in a variant of Lua called Luau. The creators form a
broad group, ranging from novices writing their first script to professional
developers; thus, Luau must support a wide audience. As part of its efforts to
support all kinds of programmers, Luau includes an optional, gradual type
system and goes to great lengths to minimize false positive errors.
  Inquiry: Since Luau is currently being used by many creators, we want to
collect data to improve the language and, in particular, the type system. The
standard way to collect data is to deploy client-side telemetry; however, we
cannot scrape personal data or proprietary information, which means we cannot
collect source code fragments, error messages, or even filepaths. The research
questions are thus about how to conduct telemetry that is not invasive and
obtain insights from it about type errors.
  Approach: We designed and implemented a pseudonymized, randomly-sampling
telemetry system for Luau. Telemetry records include a timestamp, a session id,
a reason for sending, and a numeric summary of the most recent type analyses.
This information lets us study type errors over time without revealing private
data. We deployed the system in Roblox Studio during Spring 2023 and collected
over 1.5 million telemetry records from over 340,000 sessions.
  Knowledge: We present several findings about Luau, all of which suggest that
telemetry is an effective way to study type error pragmatics. One of the
less-surprising findings is that opt-in gradual types are unpopular: there is
an 100x gap between the number of untyped Luau sessions and the number of typed
ones. One surprise is that the strict mode for type analysis is overly
conservative about interactions with data assets. A reassuring finding is that
type analysis rarely hits its internal limits on problem size.
  Grounding: Our findings are supported by a dataset of over 1.5 million
telemetry records. The data and scripts for analyzing it are available in an
artifact.
  Importance: Beyond the immediate benefits to Luau, our findings about types
and type errors have implications for adoption and ergonomics in other gradual
languages such as TypeScript, Elixir, and Typed Racket. Our telemetry design is
of broad interest, as it reports on type errors without revealing sensitive
information.
