Software Defect Detection Method Based on Improved Whale Algorithm to Optimize SVM

doi:10.3969/j.issn.1671-1122.2024.08.002

Abstract

Abstract:

To enhance the performance of software defect detection, a refined model called LFWOA-SVM has been proposed, utilizing an improved Whale algorithm to optimize traditional SVM. This approach aims at inherent issues of SVM, such as low classification accuracy and complex parameter tuning. First, in view of the problems of slow convergence speed, low optimization efficiency and local optimal solution in the whale algorithm during the solution process, the whale foraging stage was optimized based on the levy flight strategy to maximize the diversification of search agents, and a hybrid mutation perturbation was proposed operators were used to improve WOA’s global optimization capabilities. Secondly, the improved whale algorithm LFWOA was used to optimize the penalty factor and kernel function parameters of SVM, which can be effectively used in software defect detection while obtaining the optimal parameters. Finally, data simulation experiments show that among 6 benchmark test functions, LFWOA exhibits higher optimization speed and global search capabilities; tests on 8 public software defect data sets show that LFWOA-SVM method can effectively improve identification performance and prediction accuracy.

Key words: software defect detection, Levy flight, whale optimization algorithm, mutation perturbation, SVM

CLC Number:

TP309

DU Ye, TIAN Xiaoqing, LI Ang, LI Meihong. Software Defect Detection Method Based on Improved Whale Algorithm to Optimize SVM[J]. Netinfo Security, 2024, 24(8): 1152-1162.

Figures/Tables 11

References 27

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类型	函数	维度	搜索范围
单峰	${{F}_{1}}(x)=\sum\limits_{i=1}^{n}{x_{i}^{2}}$	30	[-100,100]
	${{F}_{2}}(x)=\sum\limits_{i=1}^{n}{{{\left( \sum\limits_{j=1}^{n}{{{x}_{j}}} \right)}^{2}}}$	30	[-30,30]
	${{F}_{3}}(x)=\max \left\{ \left\| {{x}_{i}} \right\| \right\}$	30	[-100,100]
多峰	$\begin{align} & {{F}_{4}}(x)=-20\exp \left( -0.2\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{\left\| x_{i}^{2} \right\|}} \right)+20+e \\ & \ \ -\exp \left( \frac{1}{n}\sum\limits_{i=1}^{n}{\cos (2\text{ }\!\!\pi\!\!\text{ }{{x}_{i}})} \right) \end{align}$	30	[-32,32]
	${{F}_{5}}(x)=\frac{1}{4000}\sum\limits_{i=1}^{n}{x_{i}^{2}}-\prod\limits_{i=1}^{n}{\cos \left( \frac{{{x}_{i}}}{\sqrt{i}} \right)+1}$	30	[-600,600]
	${{F}_{6}}(x)=\frac{1}{4000}\sum\limits_{i=1}^{n}{\left[ x_{i}^{2}-10\cos (2\text{ }\!\!\pi\!\!\text{ }{{x}_{i}})+10 \right]}$	30	[-5.12,5.12]

数据集	特征数/个	数据量/个	类别
MC2	39	125	2
PC1	21	1108	2
KC3	39	458	2
MW1	39	403	2
PC3	37	1053	2
tomcat	20	858	2
jedit-4.2	20	367	2
camel-1.6	20	965	2

基准函数	算法	均值	标准差	最优解
F₁	LFWOA	0.0000E+00	0.0000E+00	0.0000E+00
	GWO	1.1788E-27	1.4877E-27	2.8560E-29
	WOA	2.6161E-28	6.4843E-28	1.0315E-32
	PSO	8.4296E-01	3.6406E-01	2.2663E-01
	MS-WOA	0.0000E+00	0.0000E+00	0.0000E+00
F₂	LFWOA	0.0000E+00	0.0000E+00	0.0000E+00
	GWO	1.8138E-05	3.5241E-05	9.9501E-09
	WOA	6.6487E+02	6.8947E+02	2.3600E+01
	PSO	7.2326E+02	8.1981E+02	6.2506E+01
	MS-WOA	7.3821E-77	2.9200E-76	6.4254E-88
F₃	LFWOA	0.0000E+00	0.0000E+00	0.0000E+00
	GWO	6.7930E-07	6.7869E-07	4.9692E-08
	WOA	5.7282E-02	9.7826E-02	4.0602E-04
	PSO	3.7070E+00	1.3278E+00	1.5407E+00
	MS-WOA	8.7712E-48	2.8337E-47	1.2352E-52
F₄	LFWOA	0.0000E+00	0.0000E+00	0.0000E+00
	GWO	3.0885E+00	3.9162E+00	5.6843E-14
	WOA	6.2138E+01	4.2734E+01	1.9973E+00
	PSO	5.2677E+01	1.2228E+01	3.4609E+01
	MS-WOA	0.0000E+00	0.0000E+00	0.0000E+00
F₅	LFWOA	0.0000E+00	0.0000E+00	8.8818E-16
	GWO	5.6843E-14	5.6843E-14	7.5495E-14
	WOA	1.9973E+00	1.9973E+00	2.2204E-14
	PSO	3.4609E+01	3.4609E+01	3.3968E+00
	MS-WOA	0.0000E+00	0.0000E+00	8.8818E-16
F₆	LFWOA	0.0000E+00	0.0000E+00	0.0000E+00
	GWO	5.8887E-03	9.2274E-03	0.0000E+00
	WOA	2.4726E-03	6.7279E-03	0.0000E+00
	PSO	2.0872E+02	1.7547E+01	1.7169E+02
	MS-WOA	5.5940E-13	3.0640E-12	0.0000E+00

数据集	SVM	WOA-SVM	PSO-SVM	LFWOA-SVM
MC2	0.538	0.600	0.567	0.638
PC1	0.581	0.664	0.663	0.671
MW1	0.500	0.643	0.639	0.643
KC3	0.500	0.538	0.542	0.551
PC3	0.757	0.762	0.675	0.764
tomcat	0.793	0.803	0.732	0.794
jedit-4.2	0.706	0.801	0.726	0.804
camel-1.6	0.611	0.631	0.597	0.673
均值	0.623	0.680	0.642	0.693

数据集	SVM	PSO-SVM	WOA-SVM	LFWOA-SVM
MC2	0.077	0.364	0.311	0.386
PC1	0.316	0.384	0.480	0.499
MW1	0.039	0.381	0.312	0.468
KC3	0.001	0.258	0.220	0.143
PC3	0.003	0.202	0.205	0.324
tomcat	0.002	0.147	0.116	0.152
jedit-4.2	0.220	0.401	0.411	0.415
camel-1.6	0.042	0.149	0.175	0.181
均值	0.086	0.286	0.279	0.321