21 citations · 31 across the 18 of their papers we have counts for
28 papers
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…
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…
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages
Fuxiang Chen, Fatemeh Fard, David Lo +1
A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves high…
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…
Reflekt: a Library for Compile-Time Reflection in Kotlin
Anastasiia Birillo, Elena Lyulina, Maria Malysheva +2
Reflection in Kotlin is a powerful mechanism to introspect program behavior during its execution at run-time. However, among the variety of practical tasks involving reflection, th…