TeReKG: A temporal collaborative knowledge graph framework for software team recommendation
Issued Date
2024-04-08
Resource Type
ISSN
09507051
Scopus ID
2-s2.0-85185306661
Journal Title
Knowledge-Based Systems
Volume
289
Rights Holder(s)
SCOPUS
Bibliographic Citation
Knowledge-Based Systems Vol.289 (2024)
Suggested Citation
Ruenin P., Choetkiertikul M., Supratak A., Tuarob S. TeReKG: A temporal collaborative knowledge graph framework for software team recommendation. Knowledge-Based Systems Vol.289 (2024). doi:10.1016/j.knosys.2024.111492 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97354
Title
TeReKG: A temporal collaborative knowledge graph framework for software team recommendation
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Author's Affiliation
Corresponding Author(s)
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Abstract
Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member's competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.