88 citations · 136 across the 34 of their papers we have counts for
6 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…
All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs
Vitaliy Bibaev, Alexey Kalina, Vadim Lomshakov +4
In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion can…
Aggregation of Stack Trace Similarities for Crash Report Deduplication
Nikolay Karasov, Aleksandr Khvorov, Roman Vasiliev +2
The automatic collection of stack traces in bug tracking systems is an integral part of many software projects and their maintenance. However, such reports often contain a lot of d…
A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts
Konstantin Grotov, Sergey Titov, Vladimir Sotnikov +2
In recent years, Jupyter notebooks have grown in popularity in several domains of software engineering, such as data science, machine learning, and computer science education. Thei…
Lupa: A Framework for Large Scale Analysis of the Programming Language Usage
Anna Vlasova, Maria Tigina, Ilya Vlasov +3
In this paper, we present Lupa - a framework for large-scale analysis of the programming language usage. Lupa is a command line tool that uses the power of the IntelliJ Platform un…
DapStep: Deep Assignee Prediction for Stack Trace Error rePresentation
Denis Sushentsev, Aleksandr Khvorov, Roman Vasiliev +2
The task of finding the best developer to fix a bug is called bug triage. Most of the existing approaches consider the bug triage task as a classification problem, however, classif…