TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence

Zhifang Liao; Zexuan Wu; Jinsong Wu; Yan Zhang; Junyi Liu; Jun Long

doi:10.1109/GLOBECOM38437.2019.9014249

TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence

Zhifang Liao, Zexuan Wu, Jinsong Wu, Yan Zhang, Junyi Liu, Jun Long^*

^*Corresponding author for this work

Computing

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

5 Citations (Scopus)

Abstract

Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. Whatâ™s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.

Original language	English
Title of host publication	2019 IEEE Global Communications Conference (GLOBECOM)
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728109626
ISBN (Print)	9781728109633
DOIs	https://doi.org/10.1109/GLOBECOM38437.2019.9014249
Publication status	Published - 27 Feb 2020
Event	2019 IEEE Global Communications Conference - Waikoloa, United States Duration: 9 Dec 2019 → 13 Dec 2019 https://globecom2019.ieee-globecom.org/ (Link to conference website)

Publication series

Name	2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

Conference

Conference	2019 IEEE Global Communications Conference
Abbreviated title	GLOBECOM 2019
Country/Territory	United States
City	Waikoloa
Period	9/12/19 → 13/12/19
Internet address	https://globecom2019.ieee-globecom.org/ (Link to conference website)

Keywords

GiHub
Influence Network
Pull request
Topic

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Signal Processing
Information Systems and Management
Safety, Risk, Reliability and Quality
Media Technology
Health Informatics

Documents and Links

10.1109/GLOBECOM38437.2019.9014249

Cite this

Liao, Z., Wu, Z., Wu, J., Zhang, Y., Liu, J., & Long, J. (2020). TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence. In 2019 IEEE Global Communications Conference (GLOBECOM) Article 9014249 (2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOBECOM38437.2019.9014249

@inproceedings{893c9a4c607b410bac3da94af1ae9c19,

title = "TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence",

abstract = "Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. What{\^a}{\texttrademark}s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.",

keywords = "GiHub, Influence Network, Pull request, Topic",

author = "Zhifang Liao and Zexuan Wu and Jinsong Wu and Yan Zhang and Junyi Liu and Jun Long",

year = "2020",

month = feb,

day = "27",

doi = "10.1109/GLOBECOM38437.2019.9014249",

language = "English",

isbn = "9781728109633",

series = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE Global Communications Conference (GLOBECOM)",

address = "United States",

note = "2019 IEEE Global Communications Conference, GLOBECOM 2019 ; Conference date: 09-12-2019 Through 13-12-2019",

url = "https://globecom2019.ieee-globecom.org/",

}

Liao, Z, Wu, Z, Wu, J, Zhang, Y, Liu, J & Long, J 2020, TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence. in 2019 IEEE Global Communications Conference (GLOBECOM)., 9014249, 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Global Communications Conference, Waikoloa, Hawaii, United States, 9/12/19. https://doi.org/10.1109/GLOBECOM38437.2019.9014249

TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence. / Liao, Zhifang; Wu, Zexuan; Wu, Jinsong et al.
2019 IEEE Global Communications Conference (GLOBECOM). Institute of Electrical and Electronics Engineers Inc., 2020. 9014249 (2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings).

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

TY - GEN

T1 - TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence

AU - Liao, Zhifang

AU - Wu, Zexuan

AU - Wu, Jinsong

AU - Zhang, Yan

AU - Liu, Junyi

AU - Long, Jun

PY - 2020/2/27

Y1 - 2020/2/27

N2 - Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. Whatâ™s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.

AB - Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. Whatâ™s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.

KW - GiHub

KW - Influence Network

KW - Pull request

KW - Topic

U2 - 10.1109/GLOBECOM38437.2019.9014249

DO - 10.1109/GLOBECOM38437.2019.9014249

M3 - Conference contribution

AN - SCOPUS:85081953937

SN - 9781728109633

T3 - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

BT - 2019 IEEE Global Communications Conference (GLOBECOM)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Global Communications Conference

Y2 - 9 December 2019 through 13 December 2019

ER -

Liao Z, Wu Z, Wu J, Zhang Y, Liu J, Long J. TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence. In 2019 IEEE Global Communications Conference (GLOBECOM). Institute of Electrical and Electronics Engineers Inc. 2020. 9014249. (2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings). doi: 10.1109/GLOBECOM38437.2019.9014249

TIRR: a code reviewer recommendation algorithm with topic model and reviewer influence

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Documents and Links

Fingerprint

Cite this