چکیده مقاله
Software bug prediction aims to automatically identify code modules likely to contain defects, improving quality and reducing testing costs Traditional methods use static code metrics complexity, churn with classical machine learning, but these often ignore inter module relationships Recently, deep learning DL techniques have shown promise by learning features from code text and structure In particular, Graph Neural Networks GNNs can model software as graphs e g classes/modules as nodes and dependencies as edges to capture rich semantics This paper reviews both traditional and DL based defect prediction approaches, with emphasis on innovative GNN models using static analysis and code dependency graphs We summarize that DL models e g LSTM, CNN often outperform conventional ML, and that GNNs leveraging code structure yield further gains in accuracy and F1 score In existing studies, LSTM based classifiers achieved ~87% accuracy, and multi view GNN approaches report 17–45% relative F1 improvement These results suggest that combining static metrics with code dependency graphs in GNN frameworks can significantly enhance bug prediction
کلیدواژهها
نویسندگان
Alireza Rahimipour Anaraki
Faculty of Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
شیوه ارجاع
Rahimipour Anaraki, Alireza,1404,Analysis and Prediction of Software Bugs Using Deep Learning Models and Code Dependency Graphs,21st International Conference on Innovation and Research in Engineering Sciences
ارائهشده در
مجموعه مقالات بیست و یکمین کنفرانس بین المللی نوآوری و تحقیق در علوم مهندسی31 تیر 1404