Research group

Machine Learning Methods in Software Engineering

Code Change Embeddings

Timofey BryksinInactive

We propose an approach for obtaining representations of code changes during pre-training and evaluate them on two different downstream tasks — applying changes to code and commit message generation. During pre-training, the model learns to apply the given code change in a correct way. This task requires only code changes themselves, which makes it unsupervised.

Participants

Publications

Unsupervised Learning of General-Purpose Embeddings for Code Changes

August 2021

Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, and Timofey Bryksin

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