An Abstract Syntax Tree Encoding Method for Cross-Project Defect Prediction

Author:

Cai ZiyiORCID,Lu LuORCID,Qiu ShaojianORCID

Funder

National Natural Science Foundation of China

Guangdong Province Application Major Fund

Guangzhou Produce and Research Fund

Zhongshan Collaborative Innovation Center Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 21 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Development of Code Evaluation System based on Abstract Syntax Tree;Journal of Technical Education Science;2024-02-28

2. AIoT-Enabled Precision Agriculture for Sustainable Crop Disease Management: Advancing SDGs Through Graph Attention Neural Networks;Lecture Notes on Data Engineering and Communications Technologies;2024

3. Convolutional Neural Network-Based Research on Software Engineering Defect Prediction;Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering;2023-11-03

4. Federated Learning for Collaborative Crop Disease Monitoring in Wheat Production;2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS);2023-11-01

5. SyntaxLineDP: a Line-level Software Defect Prediction Model based on Extended Syntax Information;2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS);2023-10-22

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