App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Software defect prediction with multi-granularity graph convolution and adaptive feature Fusion

Jin Haibo1
Liu Xueting1
Xiao Chenglong2
1. School of Software, Liaoning Technical University, Huludao Liaoning 125105, China
2. School of Mathematics and Computer Science, Shantou University, Shantou Guangdong 515063, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0019

Abstract

To address the shortcomings of existing methods in deep representation of code semantics and structure, this paper proposed a software defect prediction method based on multi-granularity graph convolutional network and adaptive feature fusion. The method combined GraphCodeBERT, a multi-granularity graph convolutional network, and contrastive learning to extract fine-grained semantic and structural information of code. GraphCodeBERT generated semantic-structural joint features through code sequences and variable node sequences. The multi-granularity graph convolutional network further extracted hierarchical structural semantics from local to global based on these features. Contrastive learning enhanced the discriminability of the obtained features. Finally, an adaptive gating mechanism fused handcrafted features for classification. Experimental results on the PROMISE dataset show that the proposed method achieves 3.3% and 2.0% improvements in F1-score over the best baselines in within-project and cross-project defect prediction scenarios, respectively, validating the effectiveness of multi-source feature fusion.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0019
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-26] Accepted Paper

Cite This Article

金海波, 刘雪婷, 肖成龙. 基于多粒度图卷积网络与自适应特征融合的软件缺陷预测方法 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0019. (Jin Haibo, Liu Xueting, Xiao Chenglong. Software defect prediction with multi-granularity graph convolution and adaptive feature Fusion [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0019. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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