BAFLineDP: Code Bilinear Attention Fusion Framework for Line-Level Defect Prediction

TL;DR

This paper introduces BAFLineDP, a novel line-level defect prediction framework that effectively integrates code semantics and local interactions, significantly improving defect localization accuracy over existing methods.

Contribution

It presents a new bilinear attention fusion framework for line-level defect prediction that considers code semantics and local interactions, addressing limitations of prior approaches.

Findings

Outperforms existing defect prediction methods in multiple projects

Effectively integrates code semantics and local interactions

Demonstrates robustness across diverse project datasets

Abstract

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.