베이지안 분류기를 이용한 소프트웨어 품질 분류

• 영문명: Software Quality Classification using Bayesian Classifier
• 발행기관: 한국IT서비스학회
• 저자명: 홍의석(Euyseok Hong)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제11권 제1호, 211~221쪽, 전체 11쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2012.03.31

영문 초록

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, NaïveBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

목차

Abstract

1. 서론

2. 품질 예측 모델

3. 모델 구축

4. 실험

5. 결론

참고문헌

저자소개

키워드

Software Quality Prediction Model Naï ve Bayes Bayesian Belief Network