Graph Representation Learning for AI-Enhanced Knowledge Discovery in Software Engineering

Authors

  • Gabriel Logan Department of Commuter Science, University if Harvard Author

Keywords:

Graph Representation Learning, Artificial Intelligence, Software Engineering, Graph Neural Networks, Software Knowledge Discovery, Code Analysis, Software Defect Prediction, AI-driven Software Development, Software Dependency Resolution, Machine Learning for Software Systems.

Abstract

The rapid growth of software engineering data presents challenges in extracting meaningful insights from complex codebases, software repositories, and project management workflows. Graph Representation Learning (GRL), a subset of machine learning, offers a promising approach to model and analyze software systems as graphs, enabling efficient knowledge discovery and automation. This study explores how AI-driven GRL techniques enhance software engineering tasks, including bug prediction, code similarity analysis, software dependency resolution, and developer recommendation systems. We propose a novel graph-based framework that leverages Graph Neural Networks (GNNs) and Graph Attention Networks (GATs) to uncover hidden relationships in software artifacts, improving predictive accuracy and decisionmaking efficiency. Experimental evaluations on real-world open-source repositories (GitHub, Stack Overflow, and JIRA datasets) demonstrate that our approach significantly outperforms traditional methods in software defect prediction, knowledge retrieval, and software maintenance recommendations. The findings highlight GRL's transformative role in enabling AI-powered automation, optimization, and intelligent decision support in software engineering.

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Published

2012-10-16

How to Cite

Graph Representation Learning for AI-Enhanced Knowledge Discovery in Software Engineering. (2012). International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 3(1), 29-42. https://ijmlrcai.com/index.php/Journal/article/view/355

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