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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2012.1
- 수록면
- 211 - 221 (11page)
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초록· 키워드
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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, Na$\ddot{i}$veBayes(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.
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참고문헌 (16)
참고문헌 신청
Ebert, C / 1996 / Fuzzy classification for software criticality analysis / Expert Systems with Applications 11(3):323~342
Catal, C / 2011 / Software fault prediction：A literature review and current trends / Expert Systems with Applications 38(4):4626~4636
Menzies, T / 2007 / Data mining static code attributes to learn defect predictors / IEEE Trans Software Engineering 33(1):2~13
홍의석 / 2003 / 훈련데이터 집합을 사용하지 않는 소프트웨어 품질예측 모델 / 정보처리학회논문지D 10(5):689~696
홍의석 / 2011 / Support Vector Machine을 이용한 초기 소프트웨어 품질 예측 / 한국IT서비스학회지 10(2):235~245
Catal, C / 2011 / Software fault prediction：A literature review and current trends / Expert Systems with Applications 38(4):4626~4636
Menzies, T / 2007 / Data mining static code attributes to learn defect predictors / IEEE Trans Software Engineering 33(1):2~13
홍의석 / 2003 / 훈련데이터 집합을 사용하지 않는 소프트웨어 품질예측 모델 / 정보처리학회논문지D 10(5):689~696
홍의석 / 2011 / Support Vector Machine을 이용한 초기 소프트웨어 품질 예측 / 한국IT서비스학회지 10(2):235~245
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