A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Abstract

The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation-based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F-measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state-of-the-arts HFS methods.

Explainable Software Defect Prediction from Cross Company Project Metrics using Machine Learning

“…Software defect prediction(SDP) has emerged as a popular research topic over the last several decades [3], [6], [7]. Researchers have utilized various classification techniques to build these models including Logistic Regression [8], Na¨ıve Bayes classifier [9], Support Vector Machine [8], Artificial Neural Networks [10], Decision Tree Classifiers [11], Random Forest Algorithms [12], kernel PCA [13], Deep Learning [14], combination of Kernel PCA and Deep Learning [15] [16] and ensemble learning techniques [17] etc.…”

“…It is also important to understand which features to be extracted for building an effective SDP model. Recently, Balogun et al [6] addressed the fact that the high dimensionality of software metric features can affect the performance of SDP models. They conducted feasibility studies on feature selection of reliable SDP model by applying hybrid feature selection algorithms.…”

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“…One-fold is used for training while other folds are used for testing the learning algorithm. In this study, 5-fold cross-validation was adopted because model overfitting is greatly avoided [45,46]. The performance metrics adopted are accuracy, F-measure, sensitivity, precision, false-positive and receiver operating characteristics (ROC).…”