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Unsupervised learning of general-purpose embeddings for code changes. / Pravilov, Mikhail; Bogomolov, Egor; Golubev, Yaroslav; Bryksin, Timofey.

MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021. ed. / Apostolos Ampatzoglou; Daniel Feitosa; Gemma Catolino; Valentina Lenarduzzi. Association for Computing Machinery, 2021. p. 7-12.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Pravilov, M, Bogomolov, E, Golubev, Y & Bryksin, T 2021, Unsupervised learning of general-purpose embeddings for code changes. in A Ampatzoglou, D Feitosa, G Catolino & V Lenarduzzi (eds), MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021. Association for Computing Machinery, pp. 7-12, 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, MaLTESQuE 2021, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2021, Virtual, Online, Greece, 23/08/21. https://doi.org/10.1145/3472674.3473979

APA

Pravilov, M., Bogomolov, E., Golubev, Y., & Bryksin, T. (2021). Unsupervised learning of general-purpose embeddings for code changes. In A. Ampatzoglou, D. Feitosa, G. Catolino, & V. Lenarduzzi (Eds.), MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021 (pp. 7-12). Association for Computing Machinery. https://doi.org/10.1145/3472674.3473979

Vancouver

Pravilov M, Bogomolov E, Golubev Y, Bryksin T. Unsupervised learning of general-purpose embeddings for code changes. In Ampatzoglou A, Feitosa D, Catolino G, Lenarduzzi V, editors, MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021. Association for Computing Machinery. 2021. p. 7-12 https://doi.org/10.1145/3472674.3473979

Author

Pravilov, Mikhail ; Bogomolov, Egor ; Golubev, Yaroslav ; Bryksin, Timofey. / Unsupervised learning of general-purpose embeddings for code changes. MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021. editor / Apostolos Ampatzoglou ; Daniel Feitosa ; Gemma Catolino ; Valentina Lenarduzzi. Association for Computing Machinery, 2021. pp. 7-12

BibTeX

@inproceedings{0c01ad6edf7d46fb8d8720966b362f3f,
title = "Unsupervised learning of general-purpose embeddings for code changes",
abstract = "Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations 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. In the task of applying code changes, our model outperforms baseline models by 5.9 percentage points in accuracy. As for the commit message generation, our model demonstrated the same results as supervised models trained for this specific task, which indicates that it can encode code changes well and can be improved in the future by pre-training on a larger dataset of easily gathered code changes.",
keywords = "Code changes, Commit message generation, Unsupervised learning",
author = "Mikhail Pravilov and Egor Bogomolov and Yaroslav Golubev and Timofey Bryksin",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, MaLTESQuE 2021, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2021 ; Conference date: 23-08-2021",
year = "2021",
month = aug,
day = "23",
doi = "10.1145/3472674.3473979",
language = "English",
pages = "7--12",
editor = "Apostolos Ampatzoglou and Daniel Feitosa and Gemma Catolino and Valentina Lenarduzzi",
booktitle = "MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021",
publisher = "Association for Computing Machinery",
address = "United States",

}

RIS

TY - GEN

T1 - Unsupervised learning of general-purpose embeddings for code changes

AU - Pravilov, Mikhail

AU - Bogomolov, Egor

AU - Golubev, Yaroslav

AU - Bryksin, Timofey

N1 - Publisher Copyright: © 2021 ACM.

PY - 2021/8/23

Y1 - 2021/8/23

N2 - Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations 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. In the task of applying code changes, our model outperforms baseline models by 5.9 percentage points in accuracy. As for the commit message generation, our model demonstrated the same results as supervised models trained for this specific task, which indicates that it can encode code changes well and can be improved in the future by pre-training on a larger dataset of easily gathered code changes.

AB - Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations 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. In the task of applying code changes, our model outperforms baseline models by 5.9 percentage points in accuracy. As for the commit message generation, our model demonstrated the same results as supervised models trained for this specific task, which indicates that it can encode code changes well and can be improved in the future by pre-training on a larger dataset of easily gathered code changes.

KW - Code changes

KW - Commit message generation

KW - Unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=85113878057&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/39d303e5-f613-3b94-b7c1-fb3fb9b616be/

U2 - 10.1145/3472674.3473979

DO - 10.1145/3472674.3473979

M3 - Conference contribution

AN - SCOPUS:85113878057

SP - 7

EP - 12

BT - MaLTESQuE 2021 - Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021

A2 - Ampatzoglou, Apostolos

A2 - Feitosa, Daniel

A2 - Catolino, Gemma

A2 - Lenarduzzi, Valentina

PB - Association for Computing Machinery

T2 - 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, MaLTESQuE 2021, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2021

Y2 - 23 August 2021

ER -

ID: 87612403