Integrating Pull Request Comment Analysis and Developer Profiles for Expertise-Based Recommendations in Global Software Development

被引:0
作者
Zamir, Sara [1 ]
Rehman, Abdul [2 ]
Mohsin, Hufsa [3 ]
Zamir, Elif [1 ]
Abbas, Assad [1 ]
Al-Yarimi, Fuad A. M. [4 ]
机构
[1] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad 45550, Pakistan
[2] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea
[3] Shifa Tameer e Millat Univ, Dept Comp, Islamabad, Pakistan
[4] King Khalid Univ, Dept Comp Sci, Abha 61421, Saudi Arabia
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Reviews; Codes; Software development management; Recommender systems; Software; Collaboration; Social networking (online); History; Analytical models; Accuracy; Expertise recommendation; global software development; pull request reviews; comments classification;
D O I
10.1109/ACCESS.2025.3532386
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Determining a suitable software developer to match project needs within the Global software development (GSD) context requires detailed information. The complexity of this problem arises from the required combination of the developer's level of technical expertise, domain knowledge, and the extent to which they possess the collaborative skills necessary for a successful project. Typical developer recommendation systems do not consider the dynamics of expertise and cooperative nature of the tasks for assessing their correctness, often restricting themselves to extracting review comments only to measure their usefulness and suggest reviewers. This research intends to create a recommendation system using pull request review comments and selected data from developers' profiles to recommend better experts based on their dynamic expertise. Using advanced algorithm techniques, the proposed model Global Developer Expertise Recommendation System (GDERS) aims to improve the quality of captured data and substantially increase the accuracy of developer recommendations. Impressively, the proposed model significantly outperformed all other text-based classifiers TextCNN, TextRCNN, and Bilstm in this study, showing an accuracy of 91.85%. This research provides a significant achievement of recommendation systems in the global software development context that support more effective collaboration and increase the probability of project completion on time by allowing project managers to find easily accessible developers in the field with the right expertise.
引用
收藏
页码:16637 / 16648
页数:12
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