All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs

no code implementations • 21 May 2022 • Vitaliy Bibaev, Alexey Kalina, Vadim Lomshakov, Yaroslav Golubev, Alexander Bezzubov, Nikita Povarov, Timofey Bryksin

We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model.

Code Completion

S3M: Siamese Stack (Trace) Similarity Measure

1 code implementation • 18 Mar 2021 • Aleksandr Khvorov, Roman Vasiliev, George Chernishev, Irving Muller Rodrigues, Dmitrij Koznov, Nikita Povarov

These systems monitor target software, and if a crash occurs they send details to a backend application.

TraceSim: A Method for Calculating Stack Trace Similarity

2 code implementations • 26 Sep 2020 • Roman Vasiliev, Dmitrij Koznov, George Chernishev, Aleksandr Khvorov, Dmitry Luciv, Nikita Povarov

Manual triaging, however, becomes infeasible for products that have large userbases, which is the reason for many different approaches to automating this task.

Software Engineering D.2.7; D.2.5; H.m

Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler

1 code implementation • 3 Apr 2020 • Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko, Nikita Povarov

In this work, we apply anomaly detection to source code and bytecode to facilitate the development of a programming language and its compiler.

Anomaly Detection