CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code
  Authoring
Authors: name: Vijayaraghavan Murali, name: Chandra Maddila, name: Imad Ahmad, name: Michael Bolin, name: Daniel Cheng, name: Negar Ghorbani, name: Renuka Fernandez, name: Nachiappan Nagappan

The rise of large language models (LLMs) has unlocked various applications of
this technology in software development. In particular, generative LLMs have
been shown to effectively power AI-based code authoring tools that can suggest
entire statements or blocks of code during code authoring. In this paper we
present CodeCompose, an AI-assisted code authoring tool developed and deployed
at Meta internally. CodeCompose is based on the InCoder LLM that merges
generative capabilities with bi-directionality. We have scaled up CodeCompose
to serve tens of thousands of developers at Meta, across 10+ programming
languages and several coding surfaces.
  We discuss unique challenges in terms of user experience and metrics that
arise when deploying such tools in large-scale industrial settings. We present
our experience in making design decisions about the model and system
architecture for CodeCompose that addresses these challenges. Finally, we
present metrics from our large-scale deployment of CodeCompose that shows its
impact on Meta's internal code authoring experience over a 15-day time window,
where 4.5 million suggestions were made by CodeCompose. Quantitative metrics
reveal that (i) CodeCompose has an acceptance rate of 22% across several
languages, and (ii) 8% of the code typed by users of CodeCompose is through
accepting code suggestions from CodeCompose. Qualitative feedback indicates an
overwhelming 91.5% positive reception for CodeCompose. In addition to assisting
with code authoring, CodeCompose is also introducing other positive side
effects such as encouraging developers to generate more in-code documentation,
helping them with the discovery of new APIs, etc.