Proposed Hybrid Approach to Predict Software Fault Detection

Banga, Manu; Bansal, Abhay; Singh, Archana

doi:10.23940/ijpe.19.08.p4.20492061

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…In order to produce the optimal feature subset, PSO will ends when the requirements are satisfied. PSO position and velocity variations are derived from basic formulas (2) and (3) [31].…”

Section: Feature Selection 1 Pso Feature Selectionmentioning

confidence: 99%

“…On the other hand, the second formula explains how the velocity of the particle at time step is updated by considering the contributions from the personal best position (Pbest) and the global best position (Gbest) that the particle itself and the entire population have achieved respectively [31]. The PSO algorithm's performance is optimized for optimal problem solving by the adjustment of coefficients (c1 and c2) and randomization (r1 and r2) [32].…”

Section: Feature Selection 1 Pso Feature Selectionmentioning

confidence: 99%

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Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Angga Maulana Akbar,

Herteno,

Saputro

et al. 2024

j.electron.electromedical.eng.med.inform

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Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately

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“…In order to produce the optimal feature subset, PSO will ends when the requirements are satisfied. PSO position and velocity variations are derived from basic formulas (2) and (3) [31].…”

Section: Feature Selection 1 Pso Feature Selectionmentioning

confidence: 99%

Section: Feature Selection 1 Pso Feature Selectionmentioning

confidence: 99%

Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Angga Maulana Akbar,

Herteno,

Saputro

et al. 2024

j.electron.electromedical.eng.med.inform

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“…PSO feature selection is employed to find the optimal feature subset, particularly effective for datasets with noisy attributes [30], [31]. In PSO, a population of particles represents feature subsets in a binary manner (1 for inclusion, 0 for exclusion).…”

Section: Classification With Particle Swarm Optimization Feature Sele...mentioning

confidence: 99%

“…Particles adapt to personal best results (Pbest) and the overall best in the population (Gbest) during iterations, with PSO terminating when criteria are met to yield the best feature subset. PSO's position and velocity changes derive from basic formulas (2) and (3) [10], [31]:…”

Section: Classification With Particle Swarm Optimization Feature Sele...mentioning

confidence: 99%

Optimized multi correlation-based feature selection in software defect prediction

Rahman,

Nugroho,

Faisal

et al. 2024

TELKOMNIKA

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In software defect prediction, noisy attributes and high-dimensional data remain to be a critical challenge. This paper introduces a novel approach known as multi correlation-based feature selection (MCFS), which seeks to address these challenges. MCFS integrates two feature selection techniques, namely correlation-based feature selection (CFS) and correlation matrixbased feature selection (CMFS), intending to reduce data dimensionality and eliminate noisy attributes. To accomplish this, CFS and CMFS are applied independently to filter the datasets, and a weighted average of their outcomes is computed to determine the optimal feature selection. This approach not only reduces data dimensionality but also mitigates the impact of noisy attributes. To further enhance predictive performance, this paper leverages the particle swarm optimization (PSO) algorithm as a feature selection mechanism, specifically targeting improvements in the area under the curve (AUC). The evaluation of the proposed method is conducted on 12 benchmark datasets sourced from the NASA metrics data program (MDP) corpus, renowned for their noisy attributes, high dimensionality, and imbalanced class records. The research findings demonstrate that MCFS outperforms CFS and CMFS, yielding an average AUC value of 0.891, thereby emphasizing it is efficacy in advancing classification performance in the context of software defect prediction using k-nearest neighbors (KNN) classification.

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