Forschung
Machine Learning-Methoden im Software Engineering
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Projekte
E. Bogomolov
Astminer
A. Lobanov, E. Bogomolov, Y. Golubev
Codeforces Tags Prediction
A. Birillo, M. Tigina, Y. Golubev
Hyperstyle
V. Tankov
Kotless
A. Birillo, Y. Golubev, M. Tigina, A. Vlasova
Lupa
V. Tankov, A. Birillo
Reflekt
E. Bogomolov, O. Petrova, M. Evtikheev
Large-scale pre-training of graph neural networks for ML4SE tasks
M. Evtikheev, E. Bogomolov
Evaluation of performance metrics for code generation models
Mikhail Evtikheev, Egor Bogomolov
Evaluation of existing optimization methods for neural networks
A. Eliseeva, E. Bogomolov
Completion of commit messages
E. Bogomolov
Per-project fine-tuning of neural models
E. Bogomolov
IRen
E. Bogomolov
Detecting and updating inconsistent code comments
E. Bogomolov, O. Petrova
Type inference in Python
D. Litvinov
Generation of SQL queries from natural language
S. Titov, Y. Golubev
Large-scale analysis of Jupyter Notebooks
S. Titov
Code clone analysis in Jupyter notebooks
S. Titov, Y. Golubev
Non-linear Jupyter notebooks
S. Titov, Y. Golubev
Overcoming the Mental Set effect
S. Titov
Attention in programming
O. Smirnov
Code reachability study
A. Birillo, M. Tigina
Predicting static analyzer output with machine learning
A. Birillo, M. Tigina, A. Lobanov
Clustering MOOC submissions
O. Smirnov, V. Tankov
Paddle
A. Tuchina, V. Tankov
KInference
M. Tigina
Commit cleaner
M. Tigina
KotlinRMiner
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