Determining the Most Significant Metadata Features to Indicate Defective Software Commits
Author:
Affiliation:
1. The University of Tennessee,Industrial & Systems Engineering,Knoxville,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10197651/10197644/10197721.pdf?arnumber=10197721
Reference16 articles.
1. A Hybrid Feature Selection Method for Software Defect Prediction
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4. Software defect prediction: Effect of feature selection and ensemble methods;mabayoje;FUW Trends in Science and Technology Journal,2018
5. Using bandit algorithms for selecting feature reduction techniques in software defect prediction;tsunoda;2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR),2022
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