Bidirectional Program Dependency–Guided Attention for Improved Method‐Level Software Defect Prediction.
Saved in:
| Title: | Bidirectional Program Dependency–Guided Attention for Improved Method‐Level Software Defect Prediction. |
|---|---|
| Authors: | Lin, Jing‐Rong1 (AUTHOR), Huang, Chin‐Yu1,2 (AUTHOR) cyhuang@cs.nthu.edu.tw, Fang, Chih‐Chiang2 (AUTHOR) |
| Source: | Journal of Software: Evolution & Process. May2026, Vol. 38 Issue 5, p1-20. 20p. |
| Subjects: | Transformer models, Defect tracking (Computer software development), Programming language semantics, Software engineering |
| Abstract: | Software defect prediction (SDP) is an active area of research in software engineering, with numerous approaches proposed over the years to assist practitioners in identifying potential software defects. However, most existing SDP approaches predict software defects at a coarse‐grained level. Practitioners must still invest considerable time and effort to manually inspect large code segments. Another critical issue lies in the contextual understanding affected by code structures. Some SDP approaches attempt to learn the context from consecutive lines of code, which may not always constitute a meaningful semantic unit. Furthermore, the data preprocessing techniques employed in many SDP studies raise concerns about preserving code semantics. In this study, we propose bidirectional program dependency–guided attention for defect prediction (BiPDG‐DP), a hierarchical Transformer‐based language model that learns the contextual information of a method‐level PDG considering both the control and data dependencies in the directions of both source‐to‐target and target‐to‐source for method‐level SDP. Based on the comparative experimental results on the 32 releases of nine Java projects, our proposed approach outperforms other baseline approaches with significant relative improvements of 11.8%–35.7% in terms of the non‐effort‐aware evaluation metrics and 14.2%–218.5% in terms of the effort‐aware evaluation metrics, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
