YAZILIM HATA SINIFLANDIRMASINDA FARKLI NAİVE BAYES TEKNİKLERİN KIYASLANMASI

Bu çalışmada, Mutlak Bağlantılı Ağırlıklandırılmış Naive Bayes metodu, Naive Bayes metodu ve Naive Bayes sınıflandırma metodu üzerine uygulanan çeşitli yumuşatma teknikleri (Jelinek-Mercer, Dirichlet, Two-Stage) ile yazılım ölçütlerine dayalı hata sınıflandırmasının karşılaştırmalı analizi araştırılmıştır. Yapılan çalışmada Chidamber & Kemerer ve LOC metrik kümesine sahip 3 veri seti üzerinde modellerin başarımı incelenmiştir. Bu çalışma, kullanılan veri setleri/ölçüt gruplarına göre araştırılan metotlardan Naive Bayes metodu üzerine uygulanan bazı tekniklerin (Dirichlet, Two-Stage) sınıflandırma performansını diğer sınıflandırma metotlarına kıyasla daha da iyileştirdiği sonucunu gösterdi. Bu çalışmanın sonuçlarına göre, %90 üzerinde sınıflandırma doğrulukları DIT-NOC-CBO, DIT-NOC-LCOM, DIT-CBO-LCOM, NOC-CBO-LCOM ölçüt grupları için Dirichlet ve Two-Stage teknikleriyle elde edildi.

Anahtar Kelimeler:

Yazılım hata sınıflandırma, naive bayes, sınıflandırma

A COMPARISON OF DIFFERENT NAIVE BAYES TECHNIQUES FOR SOFTWARE DEFECT CLASSIFICATION

In this study was investigated that the comparative analysis of software defect classification with using Absolute Correlation Weighted Naive Bayes method, Naive Bayes method and various smoothing techniques (Jelinek-Mercer, Dirichlet, Two-Stage) on Naive Bayes classification technique. In this study, the performance of the models were examined on 3 data sets with a set of metrics Chidamber & Kemerer and LOC. The study results showed that according to used data sets/metric groups and methods the performance of some smoothing techniques (Dirichlet, Two-Stage) performs better than other classification methods. As the results of this study, over 90% classification accuracies were obtained with Dirichlet and Two-Stage smoothing techniques on DIT-NOC-CBO, DIT-NOC-LCOM, DIT-CBO-LCOM, NOC-CBO-LCOM metric groups.

Keywords:

Software defect classification, naive bayes, classification,

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