摘要 : Prediction of fault prone module prior to testing is an emerging activity for software organizations to allocate targeted resource for development of reliable software. These software fault prediction depend on the quality of faul... 展开 Prediction of fault prone module prior to testing is an emerging activity for software organizations to allocate targeted resource for development of reliable software. These software fault prediction depend on the quality of fault and related code extracted from previous versions of software. This paper, presents a novel framework by combining multiple expert machine learning systems. The proposed multi-classifier model takes the benefits of best classifiers in deciding the faulty modules of software system with consensus prior to testing. An experimental comparison is performed with various outperformer classifiers in the area of fault prediction. We evaluate our approach on 16 public dataset from promise repository which consists of National Aeronautics and Space Administration( NASA) Metric Data Program (MDP) projects and Turkish software projects. The experimental result shows that our multi classifier approach which is the combination of Support Vector Machine (SVM), Naive Bayes (NB) and Random forest machine significantly improves the performance of software fault prediction. 收起