CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code
  Authoring
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.
646c23521da1b6d027feb165
Vijayaraghavan Murali
646c23521da1b6d027feb166
Chandra Maddila
admin_assigned
2023-05-23T08:55:46.486Z
63c88fe2b9a8a4a29e4ac356
/avatars/22b5f92570d507645fc369234619eaed.svg
Chandra Maddila
user
csm550
646c23521da1b6d027feb167
Imad Ahmad
claimed_verified
2023-05-24T07:51:36.430Z
64543980548f22be5982f0f5
/avatars/aebdeff7b7f90770cfa90ffc9dc808de.svg
Imad Ahmad
user
imadahmad
646c23521da1b6d027feb168
Michael Bolin
646c23521da1b6d027feb169
Daniel Cheng
646c23521da1b6d027feb16a
Negar Ghorbani
646c23521da1b6d027feb16b
Renuka Fernandez
646c23521da1b6d027feb16c
Nachiappan Nagappan
CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code
  Authoring
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.
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.
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