The quality of the software being developed varies with the size and complexity of the software. It is a matter of concern in software development as it impairs the faith of customers on the software companies. The quality of software can be improved if the prediction of faults and flaws in it are done in the early phases of the software development and thus reducing the resources to be used in the testing phase. The rise in the use of Object-Oriented technology for developing software has paved the way for considering the Object-Oriented metrics for software fault prediction. Numerous machine learning and statistical techniques have been used to predict the defects in software using these software metrics as independent variables and bug proneness as dependent variable. Our work aims at finding the best category and hence the best classifier for classification of faults. This work uses twenty-one classifiers belonging to five categories of classification on five open source software having Object-Oriented metrics. The classification LearnerApp of MATLAB has been used to evaluate various classification models. The work proposes the use of Ensemble and SVM techniques over KNN, Regression, and Tree. The bagged trees (ensemble) and cubic (SVM) are found to be the best predictors amongst the twenty-one classifiers.

所开发软件的质量随软件的大小和复杂性而变化。在软件开发中，这是一个值得关注的问题，因为它损害了客户对软件公司的信心。如果在软件开发的早期阶段就可以预测软件中的错误和缺陷，则可以提高软件的质量，从而减少测试阶段要使用的资源。面向对象技术用于软件开发的兴起为考虑用于软件故障预测的面向对象指标铺平了道路。使用这些软件指标作为自变量，错误倾向作为因变量，已经使用了多种机器学习和统计技术来预测软件中的缺陷。我们的工作旨在找到最佳的类别，从而找到最佳的故障分类器。这项工作在具有面向对象指标的五个开源软件上使用了属于五个分类类别的21个分类器。MATLAB的分类LearnerApp已用于评估各种分类模型。这项工作提出在KNN，回归和树上使用Ensemble和SVM技术。在21个分类器中，袋装树（合奏）和立方（SVM）是最好的预测器。这项工作提出在KNN，回归和树上使用Ensemble和SVM技术。在21个分类器中，袋装树（合奏）和立方（SVM）是最好的预测器。这项工作提出在KNN，回归和树上使用Ensemble和SVM技术。在21个分类器中，袋装树（合奏）和立方（SVM）是最好的预测器。