Real-Time AI-Driven Bug De-duplication and Solution Tagging Using Graph Neural Networks

Authors

  • Alex Thomas Thomas

Keywords:

Bug De-duplication, Graph Neural Networks, Machine Learning, Natural Language Processing, Real-Time Systems, Solution Tagging.

Abstract

The process of bug tracking and resolution is a critical aspect of software development, yet it is often afflicted by redundancy and inefficiency, especially due to duplicate bug reports and inconsistent solution tagging. This review sees recent advances in AI-driven techniques, especially those utilizing Graph Neural Networks (GNNs), for real-time bug de-duplication and automated solution tagging. I investigate how the relational structures are essential in bug reports and historical fixes can be modeled using GNNs to improve bug triage processes. The review incorporates key methodologies, compares performance across multiple benchmarks, and highlights the benefits and limitations of GNN-based approaches like traditional machine learning and NLP methods. Furthermore, I analyze the combination of such models in real-world development pipelines and discuss their potential to reduce manual effort, advance in debugging workflows, and improve overall software quality. Finally, the paper recognizes open challenges and future research directions, including scalability, real-time inference, and domain adaptation, to guide future innovation in automated bug management.

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Published

19.04.2025

How to Cite

Alex Thomas Thomas. (2025). Real-Time AI-Driven Bug De-duplication and Solution Tagging Using Graph Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 13(1), 256 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7652

Issue

Vol. 13 No. 1 (2025)

Section

Research Article

License

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