Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1206-1213.DOI: 10.11772/j.issn.1001-9081.2022030444
• Computer software technology • Previous Articles Next Articles
Zhenhua YU1, Zhengqi LIU1, Ying LIU2(), Cheng GUO3
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.Supported by:
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;基金资助:
CLC Number:
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.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022030444
U | 状态 | U | 状态 |
---|---|---|---|
[0,0.1) | s1 | (0.5,1] | s3 |
[0.1,0.5] | s2 |
Tab. 1 State of population
U | 状态 | U | 状态 |
---|---|---|---|
[0,0.1) | s1 | (0.5,1] | s3 |
[0.1,0.5] | s2 |
状态 | 动作 | ||
---|---|---|---|
s1 | Q11 | Q12 | Q13 |
s2 | Q21 | Q22 | Q23 |
s3 | Q31 | Q32 | Q33 |
Tab. 3 Q-table
状态 | 动作 | ||
---|---|---|---|
s1 | Q11 | Q12 | Q13 |
s2 | Q21 | Q22 | Q23 |
s3 | Q31 | Q32 | Q33 |
实际标签 | 预测标签 | |
---|---|---|
有缺陷 | 无缺陷 | |
有缺陷 | TP | FN |
无缺陷 | FP | TN |
Tab. 4 Confusion matrix
实际标签 | 预测标签 | |
---|---|---|
有缺陷 | 无缺陷 | |
有缺陷 | TP | FN |
无缺陷 | FP | TN |
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. 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 |
算法 | 参数设置 |
---|---|
BPSO[ | 种群大小40;迭代次数100;惯性权重 |
ISCA[ | 种群大小40;迭代次数100;均衡因子 |
TMGWO[ | 种群大小40;迭代次数100;权重参数 |
TVBSSA[ | 种群大小40;迭代次数100 |
ISSA[ | 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 |
Tab. 6 Algorithm parameters
算法 | 参数设置 |
---|---|
BPSO[ | 种群大小40;迭代次数100;惯性权重 |
ISCA[ | 种群大小40;迭代次数100;均衡因子 |
TMGWO[ | 种群大小40;迭代次数100;权重参数 |
TVBSSA[ | 种群大小40;迭代次数100 |
ISSA[ | 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 |
数据集 | 平均分类精度% | 平均特征数 | ||
---|---|---|---|---|
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. 7 Experimental results of SHPSO algorithm and all features method
数据集 | 平均分类精度% | 平均特征数 | ||
---|---|---|---|---|
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 |
数据集 | 平均分类精度/% | 平均特征数 | ||||
---|---|---|---|---|---|---|
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. 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 | ||||
---|---|---|---|---|---|---|
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 |
数据集 | 平均分类精度/% | 平均特征个数 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
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 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
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 | |
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 | |
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 | |
ant | 1.71E-06 | 1.91E-06 | 6.00E-03 | 9.93E-04 | 1.06E-02 | ||
camel | 1.90E-06 | 1.68E-06 | 5.15E-03 | 4.99E-02 | 7.58E-04 | 6.55E-04 | |
ivy | 3.64E-06 | 2.47E-06 | 2.24E-02 | 4.97E-02 | 1.67E-02 |
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 | |
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 | |
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 | |
ant | 1.71E-06 | 1.91E-06 | 6.00E-03 | 9.93E-04 | 1.06E-02 | ||
camel | 1.90E-06 | 1.68E-06 | 5.15E-03 | 4.99E-02 | 7.58E-04 | 6.55E-04 | |
ivy | 3.64E-06 | 2.47E-06 | 2.24E-02 | 4.97E-02 | 1.67E-02 |
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