Next Article in Journal
New Multiple Attribute Decision Making Method Based on DEMATEL and TOPSIS for Multi-Valued Interval Neutrosophic Sets
Previous Article in Journal
Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(4), 114; doi:10.3390/sym10040114

Understanding Review Expertise of Developers: A Reviewer Recommendation Approach Based on Latent Dirichlet Allocation

1
Department of Software Technology Laboratory, Kyungpook National University, Daegu 41566, Korea
2
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Received: 21 March 2018 / Revised: 11 April 2018 / Accepted: 16 April 2018 / Published: 17 April 2018
View Full-Text   |   Download PDF [4232 KB, uploaded 17 April 2018]   |  

Abstract

The code reviewer assignment problem affects the reviewing time of a source code change. To effectively perform the code review process of a software project, the code reviewer assignment problem must be dealt with. Reviewer recommendation can reduce the time required for finding appropriate reviewers for a given source code change. In this paper, we propose a reviewer recommendation approach based on latent Dirichlet allocation (LDA). The proposed reviewer recommendation approach consists of a review expertise generation phase and a reviewer recommendation phase. The review expertise generation phase generates the review expertise of developers for topics of source code changes from the review history of a software project. The reviewer recommendation phase computes the review scores of the developers according to the topic distribution of a given source code change and the review expertise of the developers. In an empirical evaluation of five open source projects, we confirm that the proposed reviewer recommendation approach obtains better average top-10 accuracy than existing reviewer recommendation approaches. View Full-Text
Keywords: software engineering; machine learning; reviewer recommendation software engineering; machine learning; reviewer recommendation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Kim, J.; Lee, E. Understanding Review Expertise of Developers: A Reviewer Recommendation Approach Based on Latent Dirichlet Allocation. Symmetry 2018, 10, 114.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top