title: CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code
    Authoring
summary: 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.
paper_authors._id: 646c23521da1b6d027feb165
paper_authors.name: Vijayaraghavan Murali
paper_authors._id: 646c23521da1b6d027feb166
paper_authors.name: Chandra Maddila
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-23T08:55:46.486Z
paper_authors._id: 63c88fe2b9a8a4a29e4ac356
paper_authors.avatarUrl: /avatars/22b5f92570d507645fc369234619eaed.svg
paper_authors.fullname: Chandra Maddila
paper_authors.type: user
paper_authors.user: csm550
paper_authors._id: 646c23521da1b6d027feb167
paper_authors.name: Imad Ahmad
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-24T07:51:36.430Z
paper_authors._id: 64543980548f22be5982f0f5
paper_authors.avatarUrl: /avatars/aebdeff7b7f90770cfa90ffc9dc808de.svg
paper_authors.fullname: Imad Ahmad
paper_authors.type: user
paper_authors.user: imadahmad
paper_authors._id: 646c23521da1b6d027feb168
paper_authors.name: Michael Bolin
paper_authors._id: 646c23521da1b6d027feb169
paper_authors.name: Daniel Cheng
paper_authors._id: 646c23521da1b6d027feb16a
paper_authors.name: Negar Ghorbani
paper_authors._id: 646c23521da1b6d027feb16b
paper_authors.name: Renuka Fernandez
paper_authors._id: 646c23521da1b6d027feb16c
paper_authors.name: Nachiappan Nagappan
paper_title: CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code
    Authoring
paper_summary: 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.
paper_ai_summary: CodeCompose, an AI-assisted coding tool using the InCoder LLM, enhances code authoring by offering suggestions and improving documentation while achieving high user acceptance and positive feedback at Meta.
paper_ai_keywords: large language models LLMs generative LLMs AI-assisted code authoring InCoder LLM bi-directionality code authoring tool user experience model architecture system architecture acceptance rate in-code documentation API discovery