A Large-Scale Study on Source Code Reviewer Recommendation

D 2018

A Large-Scale Study on Source Code Reviewer Recommendation

LIPČÁK, Jakub and Bruno ROSSI

Basic information

Original name

A Large-Scale Study on Source Code Reviewer Recommendation

Authors

LIPČÁK, Jakub (703 Slovakia, belonging to the institution) and Bruno ROSSI (380 Italy, guarantor, belonging to the institution)

Edition

Not specified, 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018, p. 378-387, 10 pp. 2018

Publisher

IEEE

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14610/18:00102742

Organization unit

Institute of Computer Science

ISBN

978-1-5386-7383-6

ISSN

DOI

http://dx.doi.org/10.1109/SEAA.2018.00068

UT WoS

000450238900059

EID Scopus

2-s2.0-85057204726

Keywords in English

Source Code Reviewer Recommendation; Distributed Software Development; Mining Software Repositories

Abstract

In the original language

Context: Software code reviews are an important part of the development process, leading to better software quality and reduced overall costs. However, finding appropriate code reviewers is a complex and time-consuming task. Goals: In this paper, we propose a large-scale study to compare performance of two main source code reviewer recommendation algorithms (RevFinder, Naive Bayes-based) in identifying the best code reviewers for opened pull requests. Method: We mined data from Github and Gerrit repositories, building a large dataset of 51 projects, with more than 293K pull requests analyzed, 180K owners and 157K reviewers. Results: Based on the large analysis, we can state that i) no model can be generalized as best for all projects, ii) the usage of different repository (Gerrit, GitHub) has a large impact on the the recommendation results, iii) exploiting sub-projects information available in Gerrit improves the recommendation results.

Links

EF16_013/0001802, research and development project

Name: CERIT Scientific Cloud

LM2015085, research and development project

Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud

Cite

LIPČÁK, Jakub and Bruno ROSSI. A Large-Scale Study on Source Code Reviewer Recommendation. Online. In 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. Not specified: IEEE, 2018, p. 378-387. ISBN 978-1-5386-7383-6. Available from: https://dx.doi.org/10.1109/SEAA.2018.00068.

@inproceedings{1417429,
   author = {Lipčák, Jakub and Rossi, Bruno},
   address = {Not specified},
   booktitle = {44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018},
   doi = {http://dx.doi.org/10.1109/SEAA.2018.00068},
   keywords = {Source Code Reviewer Recommendation; Distributed Software Development; Mining Software Repositories},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Not specified},
   isbn = {978-1-5386-7383-6},
   pages = {378-387},
   publisher = {IEEE},
   title = {A Large-Scale Study on Source Code Reviewer Recommendation},
   url = {https://ieeexplore.ieee.org/document/8498235},
   year = {2018}
}

TY  - CONF
ID  - 1417429
AU  - Lipčák, Jakub - Rossi, Bruno
PY  - 2018
TI  - A Large-Scale Study on Source Code Reviewer Recommendation
PB  - IEEE
CY  - Not specified
SN  - 9781538673836
KW  - Source Code Reviewer Recommendation
KW  - Distributed Software Development
KW  - Mining Software Repositories
UR  - https://ieeexplore.ieee.org/document/8498235
N2  - Context: Software code reviews are an important part of the development process, leading to better software quality and reduced overall costs. However, finding appropriate code reviewers is a complex and time-consuming task. Goals: In this paper, we propose a large-scale study to compare performance of two main source code reviewer recommendation algorithms (RevFinder, Naive Bayes-based) in identifying the best code reviewers for opened pull requests. Method: We mined data from Github and Gerrit repositories, building a large dataset of 51 projects, with more than 293K pull requests analyzed, 180K owners and 157K reviewers. Results: Based on the large analysis, we can state that i) no model can be generalized as best for all projects, ii) the usage of different repository (Gerrit, GitHub) has a large impact on the the recommendation results, iii) exploiting sub-projects information available in Gerrit improves the recommendation results.
ER  -

LIPČÁK, Jakub and Bruno ROSSI. A Large-Scale Study on Source Code Reviewer Recommendation. Online. In \textit{44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018}. Not specified: IEEE, 2018, p.~378-387. ISBN~978-1-5386-7383-6. Available from: https://dx.doi.org/10.1109/SEAA.2018.00068.

Přehled o publikaci