ITU

Identifying Redundancies in Fork-based Development

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Standard

Identifying Redundancies in Fork-based Development. / Ren, Luyao; Zhou, Shurui; Kästner, Christian; Wasowski, Andrzej.

The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019. IEEE Press, 2019.

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Ren, L, Zhou, S, Kästner, C & Wasowski, A 2019, Identifying Redundancies in Fork-based Development. in The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019. IEEE Press. https://doi.org/10.1109/SANER.2019.8668023

APA

Ren, L., Zhou, S., Kästner, C., & Wasowski, A. (2019). Identifying Redundancies in Fork-based Development. In The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019 IEEE Press. https://doi.org/10.1109/SANER.2019.8668023

Vancouver

Ren L, Zhou S, Kästner C, Wasowski A. Identifying Redundancies in Fork-based Development. In The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019. IEEE Press. 2019 https://doi.org/10.1109/SANER.2019.8668023

Author

Ren, Luyao ; Zhou, Shurui ; Kästner, Christian ; Wasowski, Andrzej. / Identifying Redundancies in Fork-based Development. The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019. IEEE Press, 2019.

Bibtex

@inproceedings{a32efc1a503646ad8913c108000b211d,
title = "Identifying Redundancies in Fork-based Development",
abstract = "Fork-based development is popular and easy to use, but makes it difficult to maintain an overview of the whole community when the number of forks increases. This may lead to redundant development where multiple developers are solving the same problem in parallel without being aware of each other. Redundant development wastes effort for both maintainers and developers. In this paper, we designed an approach to identify redundant code changes in forks as early as possible by extracting clues indicating similarities between code changes, and building a machine learning model to predict redundancies. We evaluated the effectiveness from both the maintainer's and the developer's perspectives. The result shows that we achieve 57-83% precision for detecting duplicate code changes from maintainer's perspective, and we could save developers' effort of 1.9-3.0 commits on average. Also, we show that our approach significantly outperforms existing state-of-art.",
author = "Luyao Ren and Shurui Zhou and Christian K{\"a}stner and Andrzej Wasowski",
year = "2019",
doi = "10.1109/SANER.2019.8668023",
language = "English",
isbn = "978-1-7281-0592-5",
booktitle = "The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019",
publisher = "IEEE Press",

}

RIS

TY - GEN

T1 - Identifying Redundancies in Fork-based Development

AU - Ren, Luyao

AU - Zhou, Shurui

AU - Kästner, Christian

AU - Wasowski, Andrzej

PY - 2019

Y1 - 2019

N2 - Fork-based development is popular and easy to use, but makes it difficult to maintain an overview of the whole community when the number of forks increases. This may lead to redundant development where multiple developers are solving the same problem in parallel without being aware of each other. Redundant development wastes effort for both maintainers and developers. In this paper, we designed an approach to identify redundant code changes in forks as early as possible by extracting clues indicating similarities between code changes, and building a machine learning model to predict redundancies. We evaluated the effectiveness from both the maintainer's and the developer's perspectives. The result shows that we achieve 57-83% precision for detecting duplicate code changes from maintainer's perspective, and we could save developers' effort of 1.9-3.0 commits on average. Also, we show that our approach significantly outperforms existing state-of-art.

AB - Fork-based development is popular and easy to use, but makes it difficult to maintain an overview of the whole community when the number of forks increases. This may lead to redundant development where multiple developers are solving the same problem in parallel without being aware of each other. Redundant development wastes effort for both maintainers and developers. In this paper, we designed an approach to identify redundant code changes in forks as early as possible by extracting clues indicating similarities between code changes, and building a machine learning model to predict redundancies. We evaluated the effectiveness from both the maintainer's and the developer's perspectives. The result shows that we achieve 57-83% precision for detecting duplicate code changes from maintainer's perspective, and we could save developers' effort of 1.9-3.0 commits on average. Also, we show that our approach significantly outperforms existing state-of-art.

U2 - 10.1109/SANER.2019.8668023

DO - 10.1109/SANER.2019.8668023

M3 - Article in proceedings

SN - 978-1-7281-0592-5

BT - The 26th IEEE International Conference on Software Analysis Evolution and Reengineering, Hangzhou, China, Februrary 24-27, 2019

PB - IEEE Press

ER -

ID: 83700875