GCN-Based Issues Classification in Software Repository
Graph Convolutional Network (GCN) have demon- strated significant potential in various fields, particu- larly in classification tasks. This study introduces GCN- based methodology for classifying issues in software repositories, highlighting advancements in agile soft- ware development. Utilizing th...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135562 |
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| Summary: | Graph Convolutional Network (GCN) have demon- strated significant potential in various fields, particu- larly in classification tasks. This study introduces GCN- based methodology for classifying issues in software repositories, highlighting advancements in agile soft- ware development. Utilizing the dataset by Tawosi et al.(Tawosi et al. 2022), our research demonstrates the potential of GCNs to accurately categorize software is- sues into bugs, improvements, and tasks. Our results indicate a significant improvement in issue classifi- cation, especially for bugs. Additionally, we explore Fast Text GCN model, underlining their efficiency in handling dynamic, evolving datasets. This paper con- tributes to the fields of software engineering and ma- chine learning, offering novel insights into enhancing issue management in software projects. |
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| ISSN: | 2334-0754 2334-0762 |