CAJ | 학술논문

近年来，软件缺陷预测逐渐成为软件工程领域的重要内容。很多典型的机器学习方法已经被应用到软件缺陷预测中，包括SVM、随机森林、决策树和朴素贝叶斯等。早期的研究工作对所有软件产品采取相同的特征提取方式，分类效果并不理想。后来一些特征选择方法被提出，比如基于启发试的回归特征消除方法已经成功与SVM方法结合起来，取得了较好的效果。文中在现有工作基础上借鉴了RFE(回归特征消除)的思想，考虑到朴素贝叶斯方法在处理小样本分类问题时的优越性，首次将RFE与朴素贝叶斯方法结合起来，利用贝叶斯模型的特性在特征选择后将特征权值应用到对分类决策的改进中，进一步提高了分类器性能。实验采用NASA的软件缺陷数据集，并对比了其他效果较好的分类算法，体现了该算法的优越性和有效性。
근년래，연건결함예측축점성위연건공정영역적중요내용。흔다전형적궤기학습방법이경피응용도연건결함예측중，포괄SVM、수궤삼림、결책수화박소패협사등。조기적연구공작대소유연건산품채취상동적특정제취방식，분류효과병불이상。후래일사특정선택방법피제출，비여기우계발시적회귀특정소제방법이경성공여SVM방법결합기래，취득료교호적효과。문중재현유공작기출상차감료RFE(회귀특정소제)적사상，고필도박소패협사방법재처리소양본분류문제시적우월성，수차장RFE여박소패협사방법결합기래，이용패협사모형적특성재특정선택후장특정권치응용도대분류결책적개진중，진일보제고료분류기성능。실험채용NASA적연건결함수거집，병대비료기타효과교호적분류산법，체현료해산법적우월성화유효성。
In recent years,software defect prediction is becoming an important part of the software engineering field. Many typical meth-ods like SVM,random forest,decision trees and Bayes have been applied to software defect prediction. However,earlier research almost takes the same feature set to train all kinds of software products and does not achieve a desired effect. Years later,some feature selection method are proposed. For example,the method combined recursive feature elimination and SVM,has got a good effect. In this paper, based on existing work,propose an algorithm which combines recursive feature elimination and Native Bayes. This algorithm will do a se-lection of feature set before training the model according to the contribution of each feature to get the optimal feature subset to be the in-put to train the model. The experiment adopts the software defect data set of NASA. Make a comparison with other machine learning methods,the experimental results demonstrate the superiority and effectiveness of this method.