###### Abstract
In addition, some of these features (metrics) may be more relevant to the class (defective or non-defective) than others, and some may be redundant or irrelevant [17; 18]. Feature selection (FS) can be used to select high uncorrelated features from the high dimensional features. In other words, it can select those features that are more relevant and irredundant to the class label of the dataset among the features. Therefore, introducing FS methods into SDP can solve the high dimensionality problem [17; 18; 19]. FS is a vital data pre-processing step in classification processes as it improves the quality of data and consequently improves the predictive performance of the prediction models. Existing research has shown that irrelevant features, along with redundant features, can severely affect the accuracy of the defect predictors [20; 21; 22; 23]. Thus, there is a need for finding an efficient feature selection method which can identify and remove as much irrelevant and redundant information as possible, thereby leading to good predictive performance with low computational cost [24; 25]. Supervised feature selection techniques evaluate the available feature's characteristics and derive a set of pertinent characteristics based on labeled datasets. The criteria used to determine the useful feature characteristics depend on the underlining computational characteristics of the technique utilized. Filter feature-ranking (FFR) methods which are types of supervised FS methods depend on the computational characteristics of each feature by certain critical factors, and then the analyst culls some features that are congruous with a particular dataset. On the other hand, filter feature subset selection (FSS) methods (another type of supervised FS methods) search for a subset of features that have good predictive capability collectively. In this study, a comparative performance analysis of FFR and FSS methods based on different search methods are investigated to determine their respective efficacy in culling a germane set of features. Recent studies have compared the impact of FS methods on the performance of SDP [26; 27; 28; 29; 30; 31; 32]. Some studies conclude that some of the FS methods are better than others [27; 28; 30; 31], while some studies claimed that there is no significant difference between the performances of FS methods in SDP [26; 29; 32]. This contradiction and inconsistency in results by existing studies may be due to the choice of search mechanism used in FS methods. In this study, four different FFR methods (Information Gain (IG), ReliefF (RFA), Gain Ratio (GR), and Clustering Variation (CV)) based on Ranker Search method and two FFS methods: Correlation-based Feature Subset Selection (CFS) and Consistency Feature Subset Selection (CNS) based on two different search approaches: Exhaustive Search (Best First Search (BFS) and Greedy Stepwise Search (GSS) methods) and Heuristic Search (Genetic Search (GS), Bat Search (BAT), Ant Search (AS), Fire-Fly Search (FS), and Particle Swarm Optimization (PSO) method). Four classification techniques: NaiveBayes (NB), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbor (KNN) were used to evaluate the effectiveness of these FS methods. The respective models were used on five software defects dataset from the NASA repository and their predictive performances were measured comparatively based on accuracy. From our experimental results, the application of FS improves the predictive performance of the prediction models and the performance of FS methods varies across datasets and prediction models. IG recorded the best improvement on prediction models over other FFR methods, while CNS based on BFS had the best influence on prediction models based on FSS methods. Further analysis showed that prediction models based on FFR are more stable in terms of performance accuracy than other FS methods. The rest of this paper is structured as follows. Section 2 presents a literature review and analysis of existing related works.