Support Vector Machine을 이용한 초기 소프트웨어 품질 예측

• 영문명: Earlv Software Oualitv Prediction Using Support Vector Machine
• 발행기관: 한국IT서비스학회
• 저자명: 홍의석(Euyseok Hong)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제10권 제2호, 235~245쪽, 전체 11쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2011.06.30

영문 초록

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

목차

Abstract

1. 서론

2. 초기 소프트웨어 품질 예측

3. 제안 모델

4. 실험

5. 결론

참고문헌

저자소개

키워드

Software Quality Prediction Model Support Vector Machine