An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction models

被引:9
作者
Al-Laham, Mohamad [1 ]
Kassaymeh, Sofian [2 ]
Al-Betar, Mohammed Azmi [3 ,4 ]
Makhadmeh, Sharif Naser [3 ]
Albashish, Dheeb [5 ]
Alweshah, Mohammed [5 ,6 ]
机构
[1] Al Balqa Appl Univ, Amman Univ Coll, Management Informat Syst Dept, Amman, Jordan
[2] Aqaba Univ Technol, Fac Informat Technol, Software Engn Dept, Aqaba, Jordan
[3] Ajman Univ, Coll Engn & Informat Technol, Artificial Intelligence Res Ctr AIRC, Ajman, U Arab Emirates
[4] Al Balqa Appl Univ, Al Huson Univ Coll, Dept IT, Irbid, Jordan
[5] Al Balqa Appl Univ, Prince Abdullah bin Ghazi Fac Commun & Informat Te, Dept CS, Al Salt, Jordan
[6] Aqaba Univ Technol, Fac Informat Technol, Artificial Intelligence Dept, Aqaba, Jordan
关键词
Multilayer perceptron neural network; Salp swarm optimizer; Metaheuristic; Optimization; Software fault prediction; ALGORITHM; SELECTION;
D O I
10.1016/j.compeleceng.2023.108923
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Machine learning (ML) approaches were employed to tackle the software fault prediction (SFP) issue due to their consistent and rigorous performance. Multilayer perceptron (MLP) neural networks are one of the most effective ML models for prediction problems. Unfortunately, MLP suffers from chronic shortcomings related to the gradient-descent learning mechanism that can easily get stuck in local minima, leading to inappropriate control parameters. For SFP tackling, MLP boosted with an improved version of Salp Swarm Optimizer (SSA), a metaheuristic swarm intelligence technique, is presented in this research. The MLP learning approach is updated with SSA to remedy these flaws. The key benefit of such an algorithm is its ability to avoid local minima through convergence behavior. Two improvements were developed in the SSA optimization loop to link SSA functionality with MLP. The first improvement is elitism (SSA-elitism), while the second is MSSA, which stands for search improvement. The performance of proposed SSA versions is evaluated using 18 benchmark SFP datasets using ROC, sensitivity, specificity, and accuracy performance metrics. Several evaluations and validations were performed by contrasting the results of the developed versions with those of the conventional MLP, SSA, and 10 state-of-the-art approaches. The evaluations and validation findings prove that the proposed versions have superior capabilities to efficiently optimize MLP parameters, which raises the quality of their predictability.
引用
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页数:22
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