Reducing false alarms in software defect prediction by decision threshold optimization

Ayşe Tosun*, Ayşe Bener

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55 Atıf (Scopus)

Özet

Software defect data has an imbalanced and highly skewed class distribution. The misclassification costs of two classes are not equal nor are known. It is critical to find the optimum bound, i.e. threshold, which would best separate defective and defect-free classes in software data. We have applied decision threshold optimization on Naïve Bayes classifier in order to find the optimum threshold for software defect data. ROC analyses show that decision threshold optimization significantly decreases false alarms (on the average by 11%) without changing probability of detection rates.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2009 3rd International Symposium on Empirical Software Engineering and Measurement, ESEM 2009
Sayfalar477-480
Sayfa sayısı4
DOI'lar
Yayın durumuYayınlandı - 2009
Harici olarak yayınlandıEvet
Etkinlik2009 3rd International Symposium on Empirical Software Engineering and Measurement, ESEM 2009 - Lake Buena Vista, FL, United States
Süre: 15 Eki 200916 Eki 2009

Yayın serisi

Adı2009 3rd International Symposium on Empirical Software Engineering and Measurement, ESEM 2009

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???event.eventtypes.event.conference???2009 3rd International Symposium on Empirical Software Engineering and Measurement, ESEM 2009
Ülke/BölgeUnited States
ŞehirLake Buena Vista, FL
Periyot15/10/0916/10/09

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