A Defect Level Assessment Method Based on Weighted Probability Ensemble

被引：0

作者：

Xie, Lixia ^{[1
]}

Liu, Siyu ^{[1
]}

Yang, Hongyu ^{[1
,2
]}

Zhang, Liang ^{[3
]}

机构：

[1] Civil Aviat Univ China, Sch Comp Sci & Technol, Tianjin 300300, Peoples R China

[2] Civil Aviat Univ China, Sch Safety Sci & Engn, Tianjin 300300, Peoples R China

[3] Univ Arizona, Sch Informat, Tucson, AZ 85721 USA

来源：

CYBERSPACE SAFETY AND SECURITY, CSS 2022 | 2022年 / 13547卷

基金：

中国国家自然科学基金;

关键词：

Defect level assessment; Weighted probability ensemble; Misclassification penalty;

D O I：

10.1007/978-3-031-18067-5_21

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to solve the problems that the existing defect prediction methods lack the assessment of the potential defect level of samples and do not fully consider the cost impact of misclassification, a defect level assessment method based on weighted probability ensemble (DLA-WPE) is proposed. Firstly, the greedy selection method is used to select features. Then, according to the number of samples in different categories, the unequal punishment of different misclassification is calculated to obtain the misclassification punishment (MP). The weighted probability ensemble (WPE) model is built. Finally, the voting weight of each base classifier is calculated according to the MP. According to the dichotomous probability of base classifiers, the defective quantification value is calculated to obtain the defect assessment results, and the potential defects of modules are assessed. The experimental results show that the defective quantitative values and defect levels are consistent with the actual situation of the samples.

引用

页码：293 / 300

页数：8

共 13 条

[1] An Empirical Study on Heterogeneous Defect Prediction Approaches [J].

Chen, Haowen ;

Jing, Xiao-Yuan ;

Li, Zhiqiang ;

Wu, Di ;

Peng, Yi ;

Huang, Zhiguo .

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2021, 47 (12) :2803-2822

[2] Evaluating defect prediction approaches: a benchmark and an extensive comparison [J].

D'Ambros, Marco ;

Lanza, Michele ;

Robbes, Romain .

EMPIRICAL SOFTWARE ENGINEERING, 2012, 17 (4-5) :531-577

[3]

Jureczko M., 2010, P 6 INT C PREDICTIVE, P1, DOI DOI 10.1145/1868328.1868342

[4] Software defect prediction using ensemble learning on selected features [J].

Laradji, Issam H. ;

Alshayeb, Mohammad ;

Ghouti, Lahouari .

INFORMATION AND SOFTWARE TECHNOLOGY, 2015, 58 :388-402

[5] Software Defect Prediction via Convolutional Neural Network [J].

Li, Jian ;

He, Pinjia ;

Zhu, Jieming ;

Lyu, Michael R. .

2017 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS), 2017, :318-328

[6] Software Vulnerability Detection Using Deep Neural Networks: A Survey [J].

Lin, Guanjun ;

Wen, Sheng ;

Han, Qing-Long ;

Zhang, Jun ;

Xiang, Yang .

PROCEEDINGS OF THE IEEE, 2020, 108 (10) :1825-1848

[7] Data Quality: Some Comments on the NASA Software Defect Datasets [J].

Shepperd, Martin ;

Song, Qinbao ;

Sun, Zhongbin ;

Mair, Carolyn .

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2013, 39 (09) :1208-1215

[8]

The Standardization Administration of China, 2007, 20984 GBT

[9]

Thota M.K., 2020, International Journal of Applied Science and Engineering, V17, P331, DOI DOI 10.6703/IJASE.202012_17(4).331

[10] Automatically Learning Semantic Features for Defect Prediction [J].

Wang, Song ;

Liu, Taiyue ;

Tan, Lin .

2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), 2016, :297-308

← 1 2 →