Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Abstract

Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems

“…The AUC value is 1 and 107 second average computational cost. In [19], the improved versions of WOA by merging with a single-point cross-over technique are proposed that uses the five different FS techniques, i.e., random, tournament, Roulette wheel, stochastic universal sampling, and linear rank. The computational cost of the given model is high.…”

“…The most promising methods are ML algorithms such as the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) [18][19][20][21], Ensemble classifiers [22][23][24][25], and other different feature selection techniques [16,18,20,21] are also used in research [14]. Machine learning techniques are the soul of data mining, which are used successfully for solving the complicated problems either in industry or research [16].…”

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

“…WOA has shown high exploration ability. Unlike other meta-heuristic algorithms, WOA updates the position vector of a whale (solution) in the exploration stage with respect to the position vector of a randomly chosen search agent rather than the optimal search agent discovered so far [17,[34][35][36]. Like other meta-heuristic algorithms, WOA has drawbacks like early convergence and the ease of falling into the local optimum.…”

“…Hence, scholars have made several improvements to the basic version of WOA to overcome its limitation and employed it to solve various optimization problems. For instance, [35] proposed an improved version of WOA based on Natural Selection Operators and applied it as a wrapper feature selection method for software fault prediction. Mafarja and Mirjalili [17] combined WOA with simulated annealing (SA) algorithm to enhance its exploitation ability and applied their enhanced WOA-based approach for feature selection.…”

IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

“…Dataset (VDS) and Invulnerable Dataset (IDS).Performance enhancement of the presented framework is estimated by a state-of-the-art classification model, 2 baseline classifiers models are considered for this comparative analysis. Specifically, the suggested LBN model is compared with Bayesian Belief Network (BBN), Decision Tree (DT) and K-Nearest Neighbor (KNN) models[47]. After experimental simulation, the outcomes of the presented model under different techniques are tabulated as presented in Table6.…”