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• W2601502403 abstract "Fault prediction on high quality industry grade software often suffers from strong imbalanced class distribution due to a low bug rate. Previous work reports on low predictive performance, thus tuning parameters is required. As the State of the Art recommends sampling methods for imbalanced learning, we analyse effects when under- and oversampling the training data evaluated on seven different classification algorithms. Our results demonstrate settings to achieve higher performance values but the various classifiers are influenced in different ways. Furthermore, not all performance reports can be tuned at the same time." @default.
• W2601502403 created "2017-04-07" @default.
• W2601502403 creator A5011388533 @default.
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• W2601502403 date "2017-02-01" @default.
• W2601502403 modified "2023-09-29" @default.
• W2601502403 title "Performance tuning for automotive Software Fault Prediction" @default.
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• W2601502403 doi "https://doi.org/10.1109/saner.2017.7884667" @default.
• W2601502403 hasPublicationYear "2017" @default.
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