1 citations · 1 across the 2 of their papers we have counts for
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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…
Unsupervised Learning of General-Purpose Embeddings for Code Changes
Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev +1
Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations d…
PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code
Egor Spirin, Egor Bogomolov, Vladimir Kovalenko +1
The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that resea…
Sosed: a tool for finding similar software projects
Egor Bogomolov, Yaroslav Golubev, Artyom Lobanov +2
In this paper, we present Sosed, a tool for discovering similar software projects. We use fastText to compute the embeddings of subtokens into a dense space for 120,000 GitHub repo…
Building Implicit Vector Representations of Individual Coding Style
Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin +1
With the goal of facilitating team collaboration, we propose a new approach to building vector representations of individual developers by capturing their individual contribution s…