A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

Production of high quality software at lower cost can be possible by detecting defect prone software modules before the testing process. With this approach, less time and resources are required to produce a high quality software as only those modules are thoroughly tested which are predicted as defective. This paper presents a classification framework which uses Multi-Filter feature selection technique and Multi-Layer Perceptron (MLP) to predict defect prone software modules. The proposed framework works in two dimensions: 1) with oversampling technique, 2) without oversampling technique. Oversampling is introduced in the framework to analyze the effect of class imbalance issue on the performance of classification techniques. The framework is implemented by using twelve cleaned NASA MDP datasets and performance is evaluated by using: F-measure, Accuracy, MCC and ROC. According to results the proposed framework with class balancing technique performed well in all of the used datasets.