Multi-line AI-assisted Code Authoring
Authors: name: Omer Dunay, name: Daniel Cheng, name: Adam Tait, name: Parth Thakkar, name: Peter C Rigby, name: Andy Chiu, name: Imad Ahmad, name: Arun Ganesan, name: Chandra Maddila, name: Vijayaraghavan Murali, name: Ali Tayyebi, name: Nachiappan Nagappan

CodeCompose is an AI-assisted code authoring tool powered by large language
models (LLMs) that provides inline suggestions to 10's of thousands of
developers at Meta. In this paper, we present how we scaled the product from
displaying single-line suggestions to multi-line suggestions. This evolution
required us to overcome several unique challenges in improving the usability of
these suggestions for developers.
  First, we discuss how multi-line suggestions can have a 'jarring' effect, as
the LLM's suggestions constantly move around the developer's existing code,
which would otherwise result in decreased productivity and satisfaction.
  Second, multi-line suggestions take significantly longer to generate; hence
we present several innovative investments we made to reduce the perceived
latency for users. These model-hosting optimizations sped up multi-line
suggestion latency by 2.5x.
  Finally, we conduct experiments on 10's of thousands of engineers to
understand how multi-line suggestions impact the user experience and contrast
this with single-line suggestions. Our experiments reveal that (i) multi-line
suggestions account for 42% of total characters accepted (despite only
accounting for 16% for displayed suggestions) (ii) multi-line suggestions
almost doubled the percentage of keystrokes saved for users from 9% to 17%.
Multi-line CodeCompose has been rolled out to all engineers at Meta, and less
than 1% of engineers have opted out of multi-line suggestions.