AI-Assisted Code Authoring at Scale: Fine-Tuning, Deploying, and Mixed Methods Evaluation
Abstract
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 9 programming languages and several coding surfaces. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges. To release a LLM model at this scale, we needed to first ensure that it is sufficiently accurate. In a random sample of 20K source code files, depending on the language, we are able to reproduce hidden lines between 40% and 58% of the time, an improvement of 1.4× and 4.1× over a model trained only on public data. We gradually rolled CodeCompose out to developers. At the time of this writing, 16K developers have used it with 8% of their code coming directly from CodeCompose . To triangulate our numerical findings, we conduct a thematic analysis on the feedback from 70 developers. We find that 91.5% of the feedback is positive, with the most common themes being discovering APIs, dealing with boilerplate code, and accelerating coding. Meta continues to integrate this feedback into CodeCompose .
BibTeX
@article{Murali-al:FSE24,
author = {Vijayaraghavan Murali and
Chandra Shekhar Maddila and
Imad Ahmad and
Michael Bolin and
Daniel Cheng and
Negar Ghorbani and
Renuka Fernandez and
Nachiappan Nagappan and
Peter C. Rigby},
title = {{AI-Assisted} Code Authoring at Scale: {Fine-Tuning,} Deploying, and Mixed Methods Evaluation},
journal = {{PACMSE}},
volume = {1},
number = {{FSE}},
pages = {1066--1085},
year = {2024},
}