Name
Abstract | ||
|---|---|---|
Finding experts that can help address critical elements or problems in a project is a challenging task. This is especially true in global software development where there is often a need to identify developers with specific skill sets and expertise. It is also essential to identify developers that can help move the project forward. To address this, we propose an expert recommendation system to help identify and classify individuals with the expertise and skills that could collaborate on a project. To achieve this goal, we model a popular project on GitHub as a contribution network. Our approach analyzes both syntactic and semantic aspects of the data by exploring the network with machine learning algorithms and considering an ontology that can be used to extract topics from the project's key terms. Our approach looks to recommend individuals with the necessary skills, have been strong contributors in the past, and have a positive interest trend. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CSCWD49262.2021.9437785 | PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) |
Keywords | DocType | Citations |
Global Software Development, expert recommendation system, syntactic analysis, semantic analysis, ontology, contribution network, collaboration | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tales Lopes | 1 | 0 | 1.01 |
| Victor StröEle | 2 | 29 | 11.27 |
| Regina M. M. Braga | 3 | 94 | 25.25 |
| José Maria N. David | 4 | 35 | 17.83 |
| Michael A. Bauer | 5 | 13 | 3.01 |