HYDRA: Massively Compositional Model for Cross-Project Defect Prediction-Reference-Cited by-同舟云学术

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HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

Published:2016-10-01 Issue:10 Volume:42 Page:977-998
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Xia Xin,Lo David,Pan Sinno Jialin,Nagappan Nachiappan,Wang Xinyu

Funder

National Basic Research Program of China

NSFC

National Key Technology R&D Program

Ministry of Science and Technology of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

http://xplorestaging.ieee.org/ielx7/32/7590232/07435328.pdf?arnumber=7435328

Cited by 222 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. Machine learning based improved cross-project software defect prediction using new structural features in object oriented software;Applied Soft Computing;2024-11

2. On the relative value of clustering techniques for Unsupervised Effort-Aware Defect Prediction;Expert Systems with Applications;2024-07

3. Cross-project defect prediction via semantic and syntactic encoding;Empirical Software Engineering;2024-06-04

4. Software Defect Prediction Based on Deep Representation Learning of Source Code From Contextual Syntax and Semantic Graph;IEEE Transactions on Reliability;2024-06

5. A Cross-Project Defect Prediction Approach Based on Code Semantics and Cross-Version Structural Information;International Journal of Software Engineering and Knowledge Engineering;2024-05-27

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