A Large-Scale Study on Source Code Reviewer Recommendation

D 2018

A Large-Scale Study on Source Code Reviewer Recommendation

LIPČÁK, Jakub a Bruno ROSSI

Základní údaje

Originální název

A Large-Scale Study on Source Code Reviewer Recommendation

Autoři

LIPČÁK, Jakub (703 Slovensko, domácí) a Bruno ROSSI (380 Itálie, garant, domácí)

Vydání

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

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14610/18:00102742

Organizační jednotka

Ústav výpočetní techniky

ISBN

978-1-5386-7383-6

ISSN

DOI

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

UT WoS

000450238900059

Klíčová slova anglicky

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

Štítky

core_B, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 3. 5. 2019 15:50, Bruno Rossi, PhD

Anotace

V originále

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.

Návaznosti

EF16_013/0001802, projekt VaV

Název: CERIT Scientific Cloud

LM2015085, projekt VaV

Název: CERIT Scientific Cloud (Akronym: CERIT-SC)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CERIT Scientific Cloud

Citovat

LIPČÁK, Jakub a 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, s. 378-387. ISBN 978-1-5386-7383-6. Dostupné z: 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  - JOUR
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 a 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, s.~378-387. ISBN~978-1-5386-7383-6. Dostupné z: https://dx.doi.org/10.1109/SEAA.2018.00068.

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