BAFLineDP: Code Bilinear Attention Fusion Framework for Line-Level Defect Prediction
CoRR(2024)
摘要
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.
更多查看译文
关键词
line-level defect prediction,code contextual feature,code pre-trained model,bilinear attention fusion
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要