Browsing by Author "Balogun, Abdullateef"
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Item Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach(Multidisciplinary Digital Publishing Institute (MDPI), 2019-07-09) Balogun, Abdullateef; Basri, Shuib; Abdulkadir, Said Jadid; Sobri, Ahmad HashimSoftware Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performanceItem Performance Analysis of Selected Clustering Techniques for Software Defects Prediction(IEEE NIgeria Chapter, 2019-06-01) Balogun, Abdullateef; Oladele, Rufus; Mojeed, Hammed; Amin-Balogun, Barakat; Adeyemo, Victor Elijah; Aro, Taye OlalereClassification algorithms that help to predict software defects play a major role in the software engineering process. This study investigated the application and performance of clustering techniques in software defect prediction (SDP). Seven clustering techniques; Farthest First Clusterer, K-Means, X-Means, Sequential information Bottleneck, Hierarchical Clusterer, Make-Density Clusterer, and Expectation Maximization were used for the classification of 8 software defect datasets from NASA repository. Experimental results revealed that the use of clustering technique as a classification process is well established as it gave a good predictive performance. Based on average accuracy across the 8 datasets, Farthest First had the best performance of 86.16%, Hierarchical clustering had 85.50% while KMeans Clustering techniques had 72.33% respectively. Expectation Maximization (EM) (33.52%) and X-Means (48.84%) gave rather poor results and Sequential Information bottleneck (SIB) (63%) and Density-based clustering techniques (71.08%) had average performances. In addition, further comparison of classification via clustering techniques with selected standard classification techniques; k-Nearest Neighbor (kNN), Naïve Bayes (NB), and Decision Tree (DT) showed that some classification via clustering techniques (Farthest First and Hierarchical Clustering Techniques) performed considerably well and outperforms some standard classification algorithms. With this, classification via clustering techniques can be considered as an alternative approach to standard classification methods in SDP. It produced a good and competitive predictive performance in SDP with an advantage of not necessarily training a predictive model and using annotated datasets while developing the predictive model. Consequently, SDP models developed using classification via clustering techniques models can be transferred from one project to another as no training of the model is involved. This will help reduce and manage the available resources during the software development process.