115 citations · 195 across the 32 of their papers we have counts for
5 papers · 1 filter
So Much in So Little: Creating Lightweight Embeddings of Python Libraries
Yaroslav Golubev, Egor Bogomolov, Egor Bulychev +1
In software engineering, different approaches and machine learning models leverage different types of data: source code, textual information, historical data. An important part of…
Out of the BLEU: how should we assess quality of the Code Generation models?
Mikhail Evtikhiev, Egor Bogomolov, Yaroslav Sokolov +1
In recent years, researchers have created and introduced a significant number of various code generation models. As human evaluation of every new model version is unfeasible, the c…
Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection
Maksim Zubkov, Egor Spirin, Egor Bogomolov +1
Code clones are pairs of code snippets that implement similar functionality. Clone detection is a fundamental branch of automatic source code comprehension, having many application…
Evaluating the Impact of Source Code Parsers on ML4SE Models
Ilya Utkin, Egor Spirin, Egor Bogomolov +1
As researchers and practitioners apply Machine Learning to increasingly more software engineering problems, the approaches they use become more sophisticated. A lot of modern appro…
Assessing Project-Level Fine-Tuning of ML4SE Models
Egor Bogomolov, Sergey Zhuravlev, Egor Spirin +1
Machine Learning for Software Engineering (ML4SE) is an actively growing research area that focuses on methods that help programmers in their work. In order to apply the developed…