Variance of ML-based software fault predictors: are we really improving fault prediction?
Author:
Affiliation:
1. University of Duisburg-Essen,Paluno,Essen,Germany
2. University of Duisburg-Essen,Essen,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10371370/10371407/10371691.pdf?arnumber=10371691
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1. On certain integrals of Lipschitz-Hankel type involving products of bessel functions
2. Cost of poor software quality in the U.S.: A 2022 REPORT;Krasner;Consortium for Information and Software Quality (CISQ)
3. Accounting for Variance in Machine Learning Benchmarks;Bouthillier
4. Machine learning based methods for software fault prediction: A survey
5. A Systematic Survey of Just-in-Time Software Defect Prediction
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1. Deep learning based continuous integration and continuous delivery software defect prediction with effective optimization strategy;Knowledge-Based Systems;2024-07
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