Learning from Imbalanced Data for Predicting the Number of Software Defects-Reference-Cited by-同舟云学术

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Learning from Imbalanced Data for Predicting the Number of Software Defects

Published:2017-10 Issue: Volume: Page:
ISSN:
Container-title:2017 IEEE 28th International Symposium on Software Reliability Engineering (ISSRE)
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Author:

Yu Xiao,Liu Jin,Yang Zijiang,Jia Xiangyang,Ling Qi,Ye Sizhe

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8108705/8109056/08109075.pdf?arnumber=8109075

Cited by 38 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. Data preparation for Deep Learning based Code Smell Detection: A systematic literature review;Journal of Systems and Software;2024-10

2. Enhancing Deep Learning Vulnerability Detection through Imbalance Loss Functions: An Empirical Study;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

3. Bug numbers matter: An empirical study of effort‐aware defect prediction using class labels versus bug numbers;Software: Practice and Experience;2024-07-10

4. Practitioners' Expectations on Code Smell Detection;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

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

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