## 2 Related Works  
A major problem associated with SDP is the dilemma of having a large number of metrics (features). In other words, using all software metrics in training an SDP model can end up negatively affecting the predictive performance of the model. As such, many FS approaches have been proposed in addressing the selection of optimal software metrics. Some studies went to the extent on comparing these methods in order to identify the best method. However, most of these studies yielded contradictory and inconsistent conclusions on the effect of FS methods in SDP [26; 29; 32].  
[PERSON] et al. [28] performed a large scale impact analysis of twenty-eight FS methods on twenty-one commonly used classifiers. Their experiment was based on software defect datasets from the NASA and the PROMISE repositories. They concluded that correlation-based filter-FS method based on the BF search method outperforms other FS methods across the datasets. This is a good indicator that their experiment covered a large number of FS methods, classification techniques, and datasets. However, they only considered BF and GA search methods as search mechanisms for the FSS methods. There are other heuristics and meta-heuristic search methods such as BAT, AS, FS, etc. that may perform better than BF and GS in this context.  
[PERSON] and [PERSON] [26] conducted a benchmark study by empirically comparing state-of-the-art FS methods. They have considered, IG, RF, Principal Component Analysis (PCA), CFS, CNS and wrapper subset evaluation (WRP). NB and DT were deployed on Five software defect datasets and the predictive models were evaluated based on Area Under Curve (AUC). Their results showed that FS is beneficial to SDP but there was no individual best FS method for SDP. This could be partly due to the number and type of software defect datasets been considered and the choice of search mechanisms in the case of the FSS and WRP FS methods.  
[PERSON] et al. [27], regarded feature selection as a search problem in software engineering. Their study was concerned with software quality estimation and they proposed a hybrid FS approach which was based on Kolmogorov-Smirnov statistic and automatic hybrid search (AHS). Their results showed that AHS was superior to other methods, and that an elimination of 85% of software metrics may affect performances of SDP models positively or remain constant.  
[PERSON] et al. [18] performed a comparative analysis of classifiers based on FFS on SDP and their results gave credit to the usage of FFS, but there can still be further analysis using other FS methods. It has been proven empirically that wrappers obtain subsets with better performance than filter feature selection because the subsets were evaluated using a real modeling algorithm [33; 34]. [PERSON] et al. [35] have also conducted comparative experiments on FS methods based on three different FFR and WRP models on four software defect datasets. Their results showed that smaller data sets generally maintain predictability with fewer features than the original data sets.  
From the aforementioned studies, only a handful of comparative performance studies have been carried out to evaluate the efficacy of FS methods based on different search mechanisms. Therefore, there is a need to have a vital comparative evaluation of FS methods based on different search mechanisms in SDP. This is to create a better understanding of FS methods characteristics and guide researchers and analysts on the selection of search methods in FS based for SDP. In this paper, a comparative performance analysis of eighteen FS methods in SDP is presented. Each FS methods was used with four different classifiers selected based on performance and heterogeneity. The respective SDP models were tested with five software defect datasets from NASA repository and evaluated based on prediction accuracy. The performance stability of each prediction model based on FS methods was further evaluated via the coefficient of variation for each prediction models.