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.
In this paper, we introduce IntelliCode Compose – a general-purpose multilingual code completion tool which is capable of predicting sequences of code tokens of arbitrary types, generating up to entire lines of syntactically correct code. It leverages state-of-the-art generative transformer model trained on 1.2 billion lines of source code in Python, C#, JavaScript and TypeScript programming languages.
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.
Tue 10 NovDisplayed time zone: (UTC) Coordinated Universal Time change
01:00 - 01:30 | |||
01:00 2mTalk | A Comprehensive Study on Challenges in Deploying Deep Learning Based Software Research Papers Zhenpeng Chen Peking University, China, Yanbin Cao Peking University, China, Yuanqiang Liu Peking University, China, Haoyu Wang Beijing University of Posts and Telecommunications, Tao Xie Peking University, Xuanzhe Liu Peking University, China DOI Pre-print | ||
01:03 1mTalk | A First Look at the Integration of Machine Learning Models in Complex Autonomous Driving Systems: A Case Study on Apollo Industry Papers pengzi Concordia University, Canada, Jinqiu Yang Concordia University, Montreal, Canada, Tse-Hsun (Peter) Chen Concordia University, Lei Ma Kyushu University DOI | ||
01:05 1mTalk | Enhancing the Interoperability between Deep Learning Frameworks by Model Conversion Industry Papers Yu David Liu SUNY Binghamton, USA, Cheng Chen ByteDance, China, Ru Zhang Microsoft Research, Tingting Qin Microsoft Research, China, Xiang Ji Microsoft Research, China, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research DOI Pre-print | ||
01:07 1mTalk | Estimating GPU Memory Consumption of Deep Learning Models Industry Papers Yanjie Gao Microsoft Research, China, Yu David Liu SUNY Binghamton, USA, Hongyu Zhang University of Newcastle, Australia, lizhengxian Microsoft Research, China, Yonghao Zhu Microsoft Research, China, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research DOI Pre-print | ||
01:09 1mTalk | IntelliCode Compose: Code Generation using Transformer Industry Papers Alexey Svyatkovskiy Microsoft, Shao Kun Deng Microsoft Corporation, Shengyu Fu Microsoft, USA, Neel Sundaresan Microsoft Corporation DOI Pre-print | ||
01:11 1mTalk | Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving Industry Papers Jinhan Kim KAIST, Jeongil Ju Hyundai Motor Group, South Korea, Robert Feldt Chalmers University of Technology, Sweden, Shin Yoo Korea Advanced Institute of Science and Technology DOI Pre-print | ||
01:13 17m | Conversations on ML In Practice Research Papers Sidong Feng Australian National University, Australia, Tse-Hsun (Peter) Chen Concordia University, Yanbin Cao Peking University, China, Yanjie Gao Microsoft Research, China, Zhenpeng Chen Peking University, China, M: Joshua Garcia University of California, Irvine | ||