https://arxiv.org/abs/2305.12050

Skip to main content
Cornell University
We gratefully acknowledge support from
the Simons Foundation and member institutions.
 
arxiv logo > cs > arXiv:2305.12050
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Software Engineering

arXiv:2305.12050 (cs)
[Submitted on 20 May 2023]

Title:CodeCompose: A Large-Scale Industrial Deployment of AI-assisted
Code Authoring

Authors:Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad, Michael
Bolin, Daniel Cheng, Negar Ghorbani, Renuka Fernandez, Nachiappan
Nagappan
Download a PDF of the paper titled CodeCompose: A Large-Scale
Industrial Deployment of AI-assisted Code Authoring, by
Vijayaraghavan Murali and 7 other authors
Download PDF

    Abstract: 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.

Subjects: Software Engineering (cs.SE); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2305.12050 [cs.SE]
          (or arXiv:2305.12050v1 [cs.SE] for this version)
          https://doi.org/10.48550/arXiv.2305.12050
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Vijayaraghavan Murali [view email]
[v1] Sat, 20 May 2023 00:45:15 UTC (1,244 KB)
Full-text links:

Download:

  * Download a PDF of the paper titled CodeCompose: A Large-Scale
    Industrial Deployment of AI-assisted Code Authoring, by
    Vijayaraghavan Murali and 7 other authors
    PDF
  * Other formats

[by-4]
Current browse context:
cs.SE
< prev   |   next >
new | recent | 2305
Change to browse by:
cs
cs.AI

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack