Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 250200086-11.doi: 10.11896/jsjkx.250200086

• Computer Software & Architecture • Previous Articles     Next Articles

BiGCN-TL:Bipartite Graph Convolutional Neural Network Transformer Localization Model for Software Bug Partial Localization Scenarios

SHI Enyi1, CHANG Shuyu2, CHEN Kejia2,3, ZHANG Yang2, HUANG Haiping2,4   

  1. 1 Bell Honors School,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    3 Jiangsu Key Laboratory of Big Data Security & Intelligent Processing,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    4 Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing 210023,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:SHI Enyi,born in 2004,undergraduate.His main research interests include AI based security and deep learning.
    HUANG Haiping,born in 1981,Ph.D,professor,Ph.D supervisor,is a senior member of CCF(No.15253S).His main research interests include information security and data privacy in IoT.
  • Supported by:
    Major Research Plan of the National Natural Science Foundation of China(92467202),Open Fund of Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation(TK224013),Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX24_1234,KYCX23_1077) and Innovation and Entrepreneurship Training Program for College students of Jiangsu Province(202410293085Z).

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Abstract: In modern complex software projects,software bugs and code changes exhibit a “many-to-many” correspondence:a single bug is often caused by multiple code changes,and a single code change can introduce multiple bugs.As a result,bug localization is often only partial,making it difficult to trace all relevant code changes.Traditional architectures typically extract semantic features of code changes and bug reports independently,relying solely on their respective contexts.However,given the large scale of modern software projects and their intricate code dependencies,such independent semantic extraction reduces the quality and robustness of individual text representations,ultimately degrading localization performance.To achieve comprehensive tracing of code related to software bugs,this paper proposes BiGCN-TL.This model focuses on enhancing the information interaction between different textual inputs,aiming to reduce reliance on the quality of individual text features.Even in scenarios where large-scale software projects exhibit complex dependencies and challenging semantic feature extraction from a single text,BiGCN-TL leverages efficient information exchange to extract high-quality semantic representations,thereby improving localization accuracy.Firstly,based on known partial localization relationships,we fine-tune a Transformer-based pre-trained model.Then,we innovatively model software bugs and code changes as a bipartite graph,leveraging the known “many-to-many” relationships.The fine-tuned encoder is used to generate the initial node representations.Secondly,this study design a link prediction task on the bipartite graph,training a GCN and a binary classification discriminator.Through graph convolution operations and attention mechanisms,node representations are dynamically updated,emphasizing the ability to promote textual information interaction and refine global classification features.The final output is a matching prediction score.Extensive comparative experiments conducted on multiple datasets validate the superiority of BiGCN-TL over traditional approaches.Additionally,ablation studies confirm the effectiveness of each module.Furthermore,the generalizability and robustness of BiGCN-TL are further verified by exploring a variety of combinations of pre-trained models and GCNs,combined with specific and visualization analysis.

Key words: Bug localization, Pre-trained model, Link prediction, Bipartite graph, Graph neural network

CLC Number: 

  • TP391
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