A COMPARISON OF DIFFERENT NAIVE BAYES TECHNIQUES FOR SOFTWARE DEFECT CLASSIFICATION

Year 2018, , 1 - 13, 31.01.2018

Saadet Aytaç Arpacı Oya Kalıpsız

https://doi.org/10.28948/ngumuh.383709

Cited By: 3

Abstract

   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

YAZILIM HATA SINIFLANDIRMASINDA FARKLI NAİVE BAYES TEKNİKLERİN KIYASLANMASI

Abstract

   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.

Keywords

Yazılım hata sınıflandırma, naive bayes, sınıflandırma

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