Feature selection method based on self-adaptive hybrid particle swarm optimization for software defect prediction

doi:10.11772/j.issn.1001-9081.2022030444

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1206-1213.DOI: 10.11772/j.issn.1001-9081.2022030444

• Computer software technology • Previous Articles

Feature selection method based on self-adaptive hybrid particle swarm optimization for software defect prediction

Zhenhua YU¹, Zhengqi LIU¹, Ying LIU²(), Cheng GUO³

^1.College of Computer Science and Technology，Xi’an University of Science and Technology，Xi’an Shaanxi 710054，China
^2.National Key Laboratory for Complex Systems Simulation，Beijing 100101，China
^3.Xi’an Institute of Applied Optics，Xi’an Shaanxi 710065，China

Received:2022-04-08 Revised:2022-06-02 Accepted:2022-06-02 Online:2023-01-11 Published:2023-04-10
Contact: Ying LIU
About author:YU Zhenhua， born in 1977， Ph. D.， professor. His research interests include software defect prediction， cyber-physical systems.
LIU Zhengqi， born in 1997， M. S. candidate. His research interests include software defect prediction.
GUO Cheng， born in 1977， Ph. D.， research fellow. His research interests include software architecture， overall design of optoelectronic system.
Supported by:
National Natural Science Foundation of China(61873277)

基于自适应混合粒子群优化的软件缺陷预测特征选择方法

于振华¹, 刘争气¹, 刘颖²(), 郭城³

^1.西安科技大学计算机科学与技术学院, 西安 710054
^2.复杂系统仿真总体重点实验室, 北京 100101
^3.西安应用光学研究所, 西安 710065

通讯作者: 刘颖
作者简介:于振华（1977—），男，山东乳山人，教授，博士，主要研究方向：软件缺陷预测、信息物理融合系统；
刘争气（1997—），男，山东菏泽人，硕士研究生，主要研究方向：软件缺陷预测；
郭城（1977—），男，陕西紫阳人，研究员，博士，主要研究方向：软件体系结构、光电系统总体设计。
基金资助:
国家自然科学基金资助项目(61873277)

Abstract

Abstract:

Feature selection is a key step in data preprocessing for software defect prediction. Aiming at the problems of existing feature selection methods such as not significant dimension reduction performance and low classification accuracy of selected optimal feature subset， a feature selection method for software defect prediction based on Self-adaptive Hybrid Particle Swarm Optimization （SHPSO） was proposed. Firstly， combined with population partition， a self-adaptive weight update strategy based on Q-learning was designed， in which Q-learning was introduced to adaptively adjust the inertia weight according to the states of the particles. Secondly， to balance the global search ability in the early stage of the algorithm and the convergence speed in the later stage， the curve adaptivity based time-varying learning factors were proposed. Finally， a hybrid location update strategy was adopted to help particles jump out of the local optimal solution as soon as possible and increase the diversity of particles. Experiments were carried out on 12 public software defect datasets. The results show that the proposed method can effectively improve the classification accuracy of software defect prediction model and reduce the dimension of feature space compared with the method using all features， the commonly used traditional feature selection methods and the mainstream feature selection methods based on intelligent optimization algorithms. Compared with Improved Salp Swarm Algorithm （ISSA）， the proposed method increases the classification accuracy by about 1.60% on average and reduces the feature subset size by about 63.79% on average. Experimental results show that the proposed method can select a feature subset with high classification accuracy and small size.

Key words: feature selection, software defect prediction, Particle Swarm Optimization (PSO) algorithm, Sine Cosine Algorithm (SCA), Q-learning

摘要：

特征选择是软件缺陷预测中数据预处理的关键步骤。针对现有特征选择方法存在的降维效果不显著、选取的最优特征子集分类精度低等问题，提出了一种基于自适应混合粒子群优化（SHPSO）的软件缺陷预测特征选择方法。首先，结合种群划分设计了基于Q学习的自适应权重更新策略，其中引入Q学习根据粒子的状态自适应地调整惯性权重；其次，为了平衡算法前期的全局搜索能力和后期的收敛速度，提出了基于曲线自适应的时变学习因子；最后，采用混合位置更新策略帮助粒子尽快跳出局部最优解，并增加粒子的多样性。在12个公开软件缺陷数据集上进行实验验证的结果表明，与使用全部特征的方法、常用的传统特征选择方法及主流的基于智能优化算法的特征选择方法相比，所提方法在提高软件缺陷预测模型分类性能和降低特征空间维度上均取得了有效的结果。与改进樽海鞘群算法（ISSA）相比，所提方法的分类精度平均提高了约1.60%，特征子集规模平均降低了约63.79%。实验结果表明，所提方法可以选出分类精度较高且数量较少的特征子集。

关键词: 特征选择, 软件缺陷预测, 粒子群优化算法, 正余弦算法, Q学习

CLC Number:

TP311.5

Zhenhua YU, Zhengqi LIU, Ying LIU, Cheng GUO. Feature selection method based on self-adaptive hybrid particle swarm optimization for software defect prediction[J]. Journal of Computer Applications, 2023, 43(4): 1206-1213.

于振华, 刘争气, 刘颖, 郭城. 基于自适应混合粒子群优化的软件缺陷预测特征选择方法[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1206-1213.

Figures/Tables 15

Tab. 1 State of population

U	状态	U	状态
［0，0.1）	s₁	（0.5，1］	s₃
［0.1，0.5］	s₂

Tab. 2 Action definition

动作	定义	动作	定义
$a 1$	+0.1	$a 3$	-0.1
$a 2$	+0.0

Tab. 2 Action definition

动作	定义	动作	定义
$a 1$	+0.1	$a 3$	-0.1
$a 2$	+0.0

Tab. 3 Q-table

状态	动作
状态	$a 1$	$a 2$	$a 3$
s₁	Q₁₁	Q₁₂	Q₁₃
s₂	Q₂₁	Q₂₂	Q₂₃
s₃	Q₃₁	Q₃₂	Q₃₃

Tab. 3 Q-table

状态	动作
状态	$a 1$	$a 2$	$a 3$
s₁	Q₁₁	Q₁₂	Q₁₃
s₂	Q₂₁	Q₂₂	Q₂₃
s₃	Q₃₁	Q₃₂	Q₃₃

Fig. 1 Variation of learning factors during particle iteration

Fig. 2 Hybrid location update strategy

Fig. 3 Encoding and decoding of particles

Tab. 4 Confusion matrix

实际标签	预测标签
实际标签	有缺陷	无缺陷
有缺陷	TP	FN
无缺陷	FP	TN

Fig. 4 Flow of SHPSO-based feature selection algorithm

Tab. 5 Specific information of datasets

数据集	语言	粒度	特征数	样本总数	有缺陷样本数	无缺陷样本数
CM1	C	函数	37	327	42	285
KC1	C++	函数	21	1 183	314	869
KC3	Java	函数	39	194	36	158
MC2	C	函数	39	125	44	81
MW1	C	函数	37	253	27	226
PC1	C	函数	37	705	61	644
PC3	C	函数	37	1 077	134	943
PC4	C	函数	37	1 287	177	1 110
PC5	C++	函数	38	1 711	471	1 240
ant-1.7	Java	类	20	745	166	579
camel-1.6	Java	类	20	965	188	777
ivy-1.1	Java	类	20	107	45	62

Tab. 6 Algorithm parameters

算法	参数设置
BPSO^［22］	种群大小40；迭代次数100；惯性权重 $ω$ =0.9；学习因子 $c 1$ = $c 2$ =2
ISCA^［23］	种群大小40；迭代次数100；均衡因子 $α$ =0.9；精英数为2
TMGWO^［24］	种群大小40；迭代次数100；权重参数 $α$ =0.01、 $β$ =0.99；变异概率 $M p$ =0.5；搜索代理的数量为5；系数A为0~2的随机数
TVBSSA^［25］	种群大小40；迭代次数100
ISSA^［26］	种群大小40；迭代次数100；局部搜索迭代次数为10
SHPSO	种群大小40；迭代次数100；初始惯性权重 $ω$ =0.9； $c 1 m a x$ = $c 2 m a x$ =2.5； $c 1 m i n$ = $c 2 m i n$ =0.5；e=2； $α$ =0.5； $β$ =0.4；调控因子 $λ$ =0.9

Tab. 6 Algorithm parameters

算法	参数设置
BPSO^［22］	种群大小40；迭代次数100；惯性权重 $ω$ =0.9；学习因子 $c 1$ = $c 2$ =2
ISCA^［23］	种群大小40；迭代次数100；均衡因子 $α$ =0.9；精英数为2
TMGWO^［24］	种群大小40；迭代次数100；权重参数 $α$ =0.01、 $β$ =0.99；变异概率 $M p$ =0.5；搜索代理的数量为5；系数A为0~2的随机数
TVBSSA^［25］	种群大小40；迭代次数100
ISSA^［26］	种群大小40；迭代次数100；局部搜索迭代次数为10
SHPSO	种群大小40；迭代次数100；初始惯性权重 $ω$ =0.9； $c 1 m a x$ = $c 2 m a x$ =2.5； $c 1 m i n$ = $c 2 m i n$ =0.5；e=2； $α$ =0.5； $β$ =0.4；调控因子 $λ$ =0.9

Tab. 7 Experimental results of SHPSO algorithm and all features method

数据集	平均分类精度%		平均特征数
数据集	全特征方法	SHPSO	全特征方法	SHPSO
CM1	84.71	89.29	37	2.43
KC1	70.91	77.02	21	2.43
KC3	77.74	86.80	39	2.13
MC2	71.32	84.91	39	2.10
MW1	87.85	94.60	37	2.43
PC1	91.04	93.47	37	1.90
PC3	86.11	89.24	37	2.50
PC4	84.85	90.90	37	3.43
PC5	68.64	78.09	38	3.93
ant	78.97	84.39	20	1.80
camel	77.40	81.98	20	1.37
ivy	70.00	84.04	20	2.87

Tab. 8 Experimental results of SHPSO algorithm and traditional feature selection methods

数据集	平均分类精度/%			平均特征数
数据集	CFS	PCA	SHPSO	CFS	PCA	SHPSO
CM1	85.02	83.91	89.29	5	12	2.43
KC1	71.24	71.96	77.02	8	8	2.43
KC3	81.69	79.38	86.89	3	11	2.13
MC2	70.18	69.39	84.91	10	11	2.10
MW1	87.68	89.39	94.61	9	12	2.43
PC1	90.06	90.80	93.47	8	13	1.90
PC3	85.70	85.45	89.24	9	13	2.50
PC4	86.17	87.89	90.99	4	15	3.43
PC5	68.75	74.18	78.09	11	15	3.93
ant	79.57	79.23	84.39	6	13	1.80
camel	77.68	77.07	81.98	8	13	1.37
ivy	69.60	71.11	84.04	7	11	2.87

Tab. 9 Experimental results of SHPSO algorithm and feature selection methods based on intelligent optimization algorithms

数据集	平均分类精度/%						平均特征个数
数据集	BPSO	ISCA	TMGWO	TVBSSA	ISSA	SHPSO	BPSO	ISCA	TMGWO	TVBSSA	ISSA	SHPSO
CM1	88.18	88.45	88.05	87.04	88.92	89.29	8.37	2.17	2.67	1.90	7.63	2.43
KC1	76.41	76.16	75.76	74.97	76.18	77.02	4.50	2.53	3.17	3.73	4.93	2.43
KC3	84.29	85.48	84.46	82.60	84.24	86.89	10.20	2.47	3.37	3.97	7.27	2.13
MC2	80.88	81.58	79.30	77.98	80.09	84.91	11.90	3.07	3.83	9.40	9.73	2.10
MW1	92.59	93.11	92.19	91.40	93.20	94.61	9.53	2.80	2.80	5.20	7.13	2.43
PC1	92.36	92.91	92.86	91.45	92.99	93.47	7.50	2.27	2.47	1.53	6.80	1.90
PC3	88.27	88.53	88.20	87.18	88.66	89.24	8.53	2.40	2.43	2.07	8.53	2.50
PC4	88.56	89.98	89.00	86.87	89.46	90.99	8.57	3.63	3.87	3.57	8.53	3.43
PC5	76.14	77.21	77.12	75.08	77.20	78.09	9.23	4.20	4.60	6.60	9.33	3.93
ant	84.33	83.59	82.90	83.27	83.90	84.39	4.53	2.63	2.50	4.37	4.70	1.80
camel	81.37	81.51	81.03	80.46	81.62	81.98	2.67	1.50	1.57	2.20	3.57	1.37
ivy	83.54	80.71	81.21	81.11	82.42	84.04	5.47	3.27	3.90	6.37	5.50	2.87

Fig. 5 Convergence curves of SHPSO algorithm and comparison algorithms on 12 datasets

Tab. 10 p values of Wilcoxon signed-rank test for classification accuracies of SHPSO algorithm and comparison algorithms

数据集	CFS	PCA	BPSO	ISCA	TMGWO	TVBSSA	ISSA
CM1	2.47E-06	1.70E-06	1.12E-04	2.53E-03	1.31E-03	1.02E-05	2.49E-03
KC1	2.52E-06	1.71E-06	2.24E-02	3.90E-02	2.15E-04	6.54E-05	8.34E-02
KC3	2.36E-06	1.65E-06	6.06E-05	8.54E-06	4.64E-06	3.52E-06	1.56E-05
MC2	3.73E-06	1.66E-06	8.41E-05	1.05E-02	9.35E-05	5.42E-06	1.83E-04
MW1	1.66E-06	1.66E-06	4.37E-05	2.35E-03	1.11E-05	4.85E-06	1.61E-03
PC1	1.72E-06	1.65E-06	3.49E-06	1.84E-03	1.25E-02	3.88E-06	4.95E-02
PC3	1.71E-06	1.69E-06	3.86E-04	2.81E-02	2.22E-04	5.84E-06	1.63E-01
PC4	1.72E-06	1.72E-06	1.72E-06	1.43E-03	1.67E-04	2.51E-06	5.78E-04
PC5	1.73E-06	1.73E-06	1.84E-05	9.42E-03	1.10E-02	4.54E-06	1.32E-01
ant	1.71E-06	1.91E-06	7.00E-01	6.00E-03	9.93E-04	1.06E-02	6.27E-02
camel	1.90E-06	1.68E-06	5.15E-03	4.99E-02	7.58E-04	6.55E-04	2.15E-01
ivy	3.64E-06	2.47E-06	6.14E-01	2.24E-02	4.97E-02	1.67E-02	7.30E-01

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Feature selection method based on self-adaptive hybrid particle swarm optimization for software defect prediction

基于自适应混合粒子群优化的软件缺陷预测特征选择方法

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