Who does what during a code review? Datasets of OSS peer review repositories

Hamasaki, Kazuki; Kula, Raula Gaikovina; Yoshida, N.; Cruz, Ana Erika Camargo; Fujiwara, Kenji; Iida, Hajimu

doi:10.1109/msr.2013.6624003

Cited by 48 publications

(36 citation statements)

References 5 publications

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“…al [5]. For the experiment, we selected the reviews that a) are already closed ; b) have at least one approver; and c) have at least one other file besides the commit message file.…”

Section: Methodsmentioning

confidence: 99%

Improving code review effectiveness through reviewer recommendations

Thongtanunam¹,

Kula

Cruz³

et al. 2014

Proceedings of the 7th International Workshop on Cooperative and Human Aspects of Software Engineering

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Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.

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“…al [5]. For the experiment, we selected the reviews that a) are already closed ; b) have at least one approver; and c) have at least one other file besides the commit message file.…”

Section: Methodsmentioning

confidence: 99%

Improving code review effectiveness through reviewer recommendations

Thongtanunam¹,

Kula

Cruz³

et al. 2014

Proceedings of the 7th International Workshop on Cooperative and Human Aspects of Software Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yang et al [12] extended this work and brought together all of the data from five open source projects under the same representation, which allowed the analysis of data from three different aspects: people-related, process-related, and product-related. Hamasaki et al [2] proposed an approach for downloading and processing such datasets in addition to publishing the data they collected from three projects (see http://sdlab.naist.jp/reviewmining/).…”

Section: Mining Review Historiesmentioning

confidence: 99%

Will it pass? Predicting the outcome of a source code review

2018

Turk J Elec Eng & Comp Sci

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It has been observed that allowing source code changes to be made only after source code reviews has a positive impact on the quality and lifetime of the resulting software. In some cases, code review processes take quite a long time and this negatively affects software development costs and employee job satisfaction. Establishing mechanisms that predict what kind of feedback reviewers will provide and what revisions they will ask for can reduce the number of times this problem occurs. Thanks to such mechanisms, developers can improve the maturity of their code change requests before the review process starts. In this way, when they start the review, the process may advance quickly and more smoothly. In this study, as a first step towards this goal, we developed a mechanism to identify whether a change proposal would require any revisions or not for approval.

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“…-Hamasaki dataset (Hamasaki et al 2013) includes data on commits and reviews from the following projects: Android, Chromium, OpenStack and Qt. This dataset includes logs of events created by Gerrit code review system, the complete description of event model and commit metadata.…”

Section: Background and Related Workmentioning

confidence: 99%

Profile based recommendation of code reviewers

2017

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Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).

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Who does what during a code review? Datasets of OSS peer review repositories

Cited by 48 publications

References 5 publications

Improving code review effectiveness through reviewer recommendations

Improving code review effectiveness through reviewer recommendations

Will it pass? Predicting the outcome of a source code review

Profile based recommendation of code reviewers

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