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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
- 2011.1
- 수록면
- 235 - 245 (11page)
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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.
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참고문헌 (14)
참고문헌 신청
Ebert, C / 1996 / Fuzzy classification for software criticality analysis / Expert Systems with Applications 11(3):323~342
Vapnik, V / 1995 / The Nature of Statistical Learning Theory / Springer New York
Elish, K. O / 2008 / Predicting defect prone software modules using support vector machines / J. Systems Software 81(5):649~660
Catal, C / 2009 / A systematic review of software fault prediction studies / Expert Systems with Applications 36(4):7346~7354
홍의석 / 2010 / 소프트웨어 품질 예측 모델을 위한 분류 프레임워크 / 한국콘텐츠학회 논문지 10(6):134~143
Vapnik, V / 1995 / The Nature of Statistical Learning Theory / Springer New York
Elish, K. O / 2008 / Predicting defect prone software modules using support vector machines / J. Systems Software 81(5):649~660
Catal, C / 2009 / A systematic review of software fault prediction studies / Expert Systems with Applications 36(4):7346~7354
홍의석 / 2010 / 소프트웨어 품질 예측 모델을 위한 분류 프레임워크 / 한국콘텐츠학회 논문지 10(6):134~143
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