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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Tue 18 May 2021 10:01 - 10:05 at MSR Room 2 - ML and Deep Learning Chair(s): Hongyu Zhang

Code completion is one of the most widely used features of modern integrated development environments (IDEs). While deep learning has made significant progress in the statistical prediction of source code, state-of-the-art neural network models consume hundreds of megabytes of memory, bloating the development environment. We address this in two steps: first we present a modular neural framework for code completion. This allows us to explore the design space and evaluate different techniques. Second, within this framework we design a novel reranking neural completion model that combines static analysis with granular token encodings. The best neural reranking model consumes just 6 MB of RAM, — 19x less than previous models — computes a single completion in 8 ms, and achieves 90% accuracy in its top five suggestions.

Conference Day
Tue 18 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:00 - 10:50
ML and Deep LearningTechnical Papers / Data Showcase / Registered Reports at MSR Room 2
Chair(s): Hongyu ZhangThe University of Newcastle
10:01
4m
Talk
Fast and Memory-Efficient Neural Code Completion
Technical Papers
Alexey SvyatkovskiyMicrosoft, Sebastian LeeUniversity of Oxford, Anna HadjitofiAlan Turing Institute, Maik RiechertMicrosoft Research, Juliana Vicente FrancoMicrosoft Research, Miltiadis AllamanisMicrosoft Research, UK
Pre-print Media Attached
10:05
4m
Research paper
Comparative Study of Feature Reduction Techniques in Software Change Prediction
Technical Papers
Ruchika MalhotraDelhi Technological University, Ritvik KapoorDelhi Technological University, Deepti AggarwalDelhi Technological University, Priya GargDelhi Technological University
Pre-print
10:09
4m
Talk
An Empirical Study on the Usage of BERT Models for Code Completion
Technical Papers
Matteo CiniselliUniversità della Svizzera Italiana, Nathan CooperWilliam & Mary, Luca PascarellaUniversità della Svizzera italiana (USI), Denys PoshyvanykCollege of William & Mary, Massimiliano Di PentaUniversity of Sannio, Italy, Gabriele BavotaSoftware Institute, USI Università della Svizzera italiana
Pre-print
10:13
3m
Talk
ManyTypes4Py: A benchmark Python dataset for machine learning-based type inference
Data Showcase
Amir MirDelft University of Technology, Evaldas LatoskinasDelft University of Technology, Georgios GousiosFacebook & Delft University of Technology
Pre-print
10:16
3m
Talk
KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle
Data Showcase
Luigi QuarantaUniversity of Bari, Italy, Fabio CalefatoUniversity of Bari, Filippo LanubileUniversity of Bari
10:19
3m
Talk
Exploring the relationship between performance metrics and cost saving potential of defect prediction models
Registered Reports
Steffen HerboldUniversity of Göttingen
Pre-print
10:22
28m
Live Q&A
Discussions and Q&A
Technical Papers


Information for Participants
Info for MSR Room 2: