Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions

摘要

The importance of software testing for quality assurance cannot be over emphasized. The estimation of quality factors is important for minimizing the cost and improving the effectiveness of the software testing process. One of the quality factors is fault proneness, for which unfortunately there is no generalized technique available to effectively identify fault proneness. Many researchers have concentrated on how to select software metrics that are likely to indicate fault proneness. At the same time dimensionality reduction (feature selection of software metrics) also plays a vital role for the effectiveness of the model or best quality model. Feature selection is important for a variety of reasons such as generalization, performance, computational efficiency and feature interpretability. In this paper a new method for feature selection is proposed based on Decision Tree Induction. Relevant features are selected from the class level dataset based on decision tree classifiers used in the classification process. The attributes which form rules for the classifiers are taken as the relevant feature set or new feature set named Decision Tree Induction Rule based (DTIRB) feature set. Different classifiers are learned with this new data set obtained by decision tree induction process and achieved better performance. The performance of 18 classifiers is studied with the proposed method. Comparison is made with the Support Vector Machines (SVM) and RELIEF feature selection techniques. It is observed that the proposed method outperforms the other two for most of the classifiers considered. Overall improvement in classification process is also found with original feature set and reduced feature set. The proposed method has the advantage of easy interpretability and comprehensibility. Class level metrics dataset is used for evaluating the performance of the model. Receiver Operating Characteristics (ROC) and Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error measures are used as the performance measures for checking effectiveness of the model.