Finding the best learning to rank algorithms for effort-aware defect prediction-Reference-Cited by-同舟云学术

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Finding the best learning to rank algorithms for effort-aware defect prediction

Published:2023-05 Issue: Volume:157 Page:107165
ISSN:0950-5849
Container-title:Information and Software Technology
language:en
Short-container-title:Information and Software Technology

Author:

Yu Xiao^ORCID,Dai Heng,Li Li,Gu Xiaodong,Keung Jacky Wai,Bennin Kwabena Ebo,Li Fuyang,Liu Jin

Publisher

Elsevier BV

Subject

Computer Science Applications,Information Systems,Software

Reference94 articles.

1. Predicting the precise number of software defects: Are we there yet?;Yu;Inf. Softw. Technol.,2022

2. Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction;Feng;Inf. Softw. Technol.,2021

3. Effort-aware defect prediction models;Mende,2010

4. Revisiting common bug prediction findings using effort-aware models;Kamei,2010

5. A learning-to-rank approach to software defect prediction;Yang;IEEE Trans. Reliab.,2014

Cited by 10 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. On the relative value of clustering techniques for Unsupervised Effort-Aware Defect Prediction;Expert Systems with Applications;2024-07

2. Improving effort-aware defect prediction by directly learning to rank software modules;Information and Software Technology;2024-01

3. Improving the undersampling technique by optimizing the termination condition for software defect prediction;Expert Systems with Applications;2024-01

4. A multi-objective effort-aware defect prediction approach based on NSGA-II;Applied Soft Computing;2023-12

5. Software defect prediction using learning to rank approach;Scientific Reports;2023-11-02

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