In software development through integrated development environments (IDEs), code completion is one of the most widely used features. Nevertheless, majority of integrated development environments only support completion of methods and APIs, or arguments.
IntelliCode Compose is deployed as a cloud-based web service. It makes use of client-side tree-based caching, efficient parallel implementation of the beam search decoder, and compute graph optimizations to meet edit-time completion suggestion requirements in the Visual Studio Code IDE and Azure Notebook.
Our best model yields an average edit similarity of 86.7% and a perplexity of 1.82 for Python programming language.
Conference DayTue 10 NovDisplayed time zone: (UTC) Coordinated Universal Time change
01:00 - 01:30
|A Comprehensive Study on Challenges in Deploying Deep Learning Based Software|
Zhenpeng ChenPeking University, China, Yanbin CaoPeking University, China, Yuanqiang LiuPeking University, China, Haoyu WangBeijing University of Posts and Telecommunications, Tao XiePeking University, Xuanzhe LiuPeking University, ChinaDOI Pre-print
|A First Look at the Integration of Machine Learning Models in Complex Autonomous Driving Systems: A Case Study on Apollo|
pengzi Concordia University, Canada, Jinqiu YangConcordia University, Montreal, Canada, Tse-Hsun (Peter) ChenConcordia University, Lei MaKyushu UniversityDOI
|Enhancing the Interoperability between Deep Learning Frameworks by Model Conversion|
Yu David LiuSUNY Binghamton, USA, Cheng ChenByteDance, China, Ru ZhangMicrosoft Research, Tingting QinMicrosoft Research, China, Xiang JiMicrosoft Research, China, Haoxiang LinMicrosoft Research, Mao YangMicrosoft ResearchDOI
|Estimating GPU Memory Consumption of Deep Learning Models|
Yanjie GaoMicrosoft Research, China, Yu David LiuSUNY Binghamton, USA, Hongyu ZhangUniversity of Newcastle, Australia, lizhengxian Microsoft Research, China, Yonghao ZhuMicrosoft Research, China, Haoxiang LinMicrosoft Research, Mao YangMicrosoft ResearchDOI
|IntelliCode Compose: Code Generation using Transformer|
Alexey SvyatkovskiyMicrosoft, Shao Kun DengMicrosoft Corporation, Shengyu FuMicrosoft, USA, Neel SundaresanMicrosoft CorporationDOI Pre-print
|Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving|
Jinhan KimKAIST, Jeongil JuHyundai Motor Group, South Korea, Robert FeldtChalmers University of Technology, Sweden, Shin YooKorea Advanced Institute of Science and TechnologyDOI Pre-print
|Conversations on ML In Practice|