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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.07132 |
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| _version_ | 1866929240747802624 |
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| author | Qiu, Shaojian Huang, Huihao Luo, Jianxiang Kuang, Yingjie Luo, Haoyu |
| author_facet | Qiu, Shaojian Huang, Huihao Luo, Jianxiang Kuang, Yingjie Luo, Haoyu |
| contents | Software defect prediction aims to identify defect-prone code, aiding developers in optimizing testing resource allocation. Most defect prediction approaches primarily focus on coarse-grained, file-level defect prediction, which fails to provide developers with the precision required to locate defective code. Recently, some researchers have proposed fine-grained, line-level defect prediction methods. However, most of these approaches lack an in-depth consideration of the contextual semantics of code lines and neglect the local interaction information among code lines. To address the above issues, this paper presents a line-level defect prediction method grounded in a code bilinear attention fusion framework (BAFLineDP). This method discerns defective code files and lines by integrating source code line semantics, line-level context, and local interaction information between code lines and line-level context. Through an extensive analysis involving within- and cross-project defect prediction across 9 distinct projects encompassing 32 releases, our results demonstrate that BAFLineDP outperforms current advanced file-level and line-level defect prediction approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07132 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BAFLineDP: Code Bilinear Attention Fusion Framework for Line-Level Defect Prediction Qiu, Shaojian Huang, Huihao Luo, Jianxiang Kuang, Yingjie Luo, Haoyu Software Engineering Software defect prediction aims to identify defect-prone code, aiding developers in optimizing testing resource allocation. Most defect prediction approaches primarily focus on coarse-grained, file-level defect prediction, which fails to provide developers with the precision required to locate defective code. Recently, some researchers have proposed fine-grained, line-level defect prediction methods. However, most of these approaches lack an in-depth consideration of the contextual semantics of code lines and neglect the local interaction information among code lines. To address the above issues, this paper presents a line-level defect prediction method grounded in a code bilinear attention fusion framework (BAFLineDP). This method discerns defective code files and lines by integrating source code line semantics, line-level context, and local interaction information between code lines and line-level context. Through an extensive analysis involving within- and cross-project defect prediction across 9 distinct projects encompassing 32 releases, our results demonstrate that BAFLineDP outperforms current advanced file-level and line-level defect prediction approaches. |
| title | BAFLineDP: Code Bilinear Attention Fusion Framework for Line-Level Defect Prediction |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2402.07132 |