Research group

Machine Learning Methods in Software Engineering

Code Style Embeddings

Project supervisor: Vladimir Kovalenko
Status: Active

This project is aimed at designing a system that builds numerical representations of developers' individual coding style in an unsupervised manner, based on data from version control.

Building the embeddings of individual code style is one way to enable team collaboration tools to detect and track the knowledge transfer processes in teams. While this looks a far-fetched goal, this project is our take on this problem.

The paper is to appear in CHASE 2020 proceedings.