This program is tentative and subject to change.

Wed 17 Jul 2024 16:54 - 17:12 at Baobá 6 - AI4SE 2

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.

This program is tentative and subject to change.

Wed 17 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

16:00 - 18:00
16:00
18m
Talk
MonitorAssistant: Simplifying Cloud Service Monitoring via Large Language Models
Industry Papers
Zhaoyang Yu Tsinghua University, Minghua Ma Microsoft Research, Chaoyun Zhang Microsoft, Si Qin Microsoft Research, Yu Kang Microsoft Research, Chetan Bansal Microsoft Research, Saravan Rajmohan Microsoft, Yingnong Dang Microsoft Azure, Changhua Pei Computer Network Information Center at Chinese Academy of Sciences, Dan Pei Tsinghua University, Qingwei Lin Microsoft, Dongmei Zhang Microsoft Research
16:18
18m
Talk
Code-Aware Prompting: A study of Coverage guided Test Generation in Regression Setting using LLM
Research Papers
Gabriel Ryan Columbia University, Siddhartha Jain AWS AI Labs, Mingyue Shang AWS AI Labs, Shiqi Wang AWS AI Labs, Xiaofei Ma AWS AI Labs, Murali Krishna Ramanathan AWS AI Labs, Baishakhi Ray Columbia University, New York; AWS AI Lab
16:36
18m
Talk
A Machine Learning-Based Error Mitigation Approach for Reliable Software Development on IBM’s Quantum Computers
Industry Papers
Asmar Muqeet Simula Research Laboratory and University of Oslo, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Tao Yue Beihang University, Paolo Arcaini National Institute of Informatics
16:54
18m
Talk
Multi-line AI-assisted Code Authoring
Industry Papers
Omer Dunay Meta Platforms, Inc., Daniel Cheng Meta Platforms Inc., Adam Tait Meta Platforms, Inc., Parth Thakkar Meta Platforms, Inc., Peter C Rigby Meta / Concordia University, Andy Chiu Meta Platforms, Inc., Imad Ahmad Meta Platforms, Inc., Arun Ganesan Meta Platforms, Inc., Chandra Sekhar Maddila Meta Platforms, Inc., Vijayaraghavan Murali Meta Platforms Inc., Ali Tayyebi Meta Platforms Inc., Nachiappan Nagappan Meta Platforms, Inc.
17:12
18m
Talk
Combating Missed Recalls in E-commerce Search: a CoT-prompting Testing Approach
Industry Papers
Shengnan Wu School of Computer Science, Fudan University, Yongxiang Hu Fudan University, Yingchuan Wang School of Computer Science, Fudan University, Jiazhen Gu The Chinese University of Hong Kong, Jin Meng Meituan Inc., Liujie Fan Meituan Inc., Zhongshi Luan Meituan Inc., Xin Wang Fudan University, Yangfan Zhou Fudan University
17:30
18m
Talk
An Empirically Grounded Path Forward for Scenario-based Testing of Autonomous Driving Systems
Industry Papers
Qunying Song Lund University, Emelie Engstrom Lund University, Per Runeson Lund University
17:48
9m
Talk
ConDefects: A Complementary Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair
Demonstrations
Yonghao Wu Beijing University of Chemical Technology, Zheng Li Beijing University of Chemical Technology, Jie M. Zhang King's College London, Yong Liu Beijing University of Chemical Technology