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Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as:“Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”. Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.