A Hierarchical Feature Ensemble Deep Learning Approach for Software Defect Prediction

被引:2
|
作者
Zhang, Shenggang [1 ]
Jiang, Shujuan [1 ]
Yan, Yue [1 ]
机构
[1] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
基金
中国国家自然科学基金;
关键词
Software defect prediction; deep learning; abstract syntax tree; class dependency network; ensemble learning;
D O I
10.1142/S0218194023500079
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Software defect prediction can detect modules that may have defects in advance and optimize resource allocation to improve test efficiency and reduce development costs. Traditional features cannot capture deep semantic and grammatical information, which limits the further development of software defect prediction. Therefore, it has gradually become a trend to use deep learning technology to automatically learn valuable deep features from source code or relevant data. However, most software defect prediction methods based on deep learning extraction features from a single information source or only use a single deep learning model, which leads to the fact that the extracted features are not comprehensive enough to affect the final prediction performance. In view of this, this paper proposes a Hierarchical Feature Ensemble Deep Learning (HFEDL) Approach for software defect prediction. Firstly, the HFEDL approach needs to obtain three types of information sources: abstract syntax tree (AST), class dependency network (CDN) and traditional features. Then, the Convolutional Neural Network (CNN) and the Bidirectional Long Short-Term Memory based on Attention mechanism (BiLSTM+Attention) are used to extract different valuable features from the three information sources and multiple prediction sub-models are constructed. Next, all the extracted features are fused by a filter mechanism to obtain more comprehensive features and construct a fusion prediction sub-model. Finally, all the sub-models are integrated by an ensemble learning method to obtain the final prediction model. We use 11 projects in the PROMISE defect repository and evaluate our approach in both non-effort-aware and effort-aware scenarios. The experimental results show that the prediction performance of our approach is superior to state-of-the-art methods in both scenarios.
引用
收藏
页码:543 / 573
页数:31
相关论文
共 50 条
  • [31] A Survey of Software Defect Prediction Based on Deep Learning
    Nevendra, Meetesh
    Singh, Pradeep
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2022, 29 (07) : 5723 - 5748
  • [32] On the Value of Oversampling for Deep Learning in Software Defect Prediction
    Yedida, Rahul
    Menzies, Tim
    IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2022, 48 (08) : 3103 - 3116
  • [33] Software Defect Density Prediction Using Deep Learning
    Alghanim, Firas
    Azzeh, Mohammad
    El-Hassan, Ammar
    Qattous, Hazem
    IEEE ACCESS, 2022, 10 : 114629 - 114641
  • [34] Deep Feature Learning to Quantitative Prediction of Software Defects
    Qiao, Lei
    Li, Guangjie
    Yu, Daohua
    Liu, Hui
    2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021), 2021, : 1401 - 1402
  • [35] Software Visualization and Deep Transfer Learning for Effective Software Defect Prediction
    Chen, Jinyin
    Hu, Keke
    Yu, Yue
    Chen, Zhuangzhi
    Xuan, Qi
    Liu, Yi
    Filkov, Vladimir
    2020 ACM/IEEE 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2020), 2020, : 578 - 589
  • [36] Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review
    Matloob, Faseeha
    Ghazal, Taher M.
    Taleb, Nasser
    Aftab, Shabib
    Ahmad, Munir
    Khan, Muhammad Adnan
    Abbas, Sagheer
    Soomro, Tariq Rahim
    IEEE ACCESS, 2021, 9 : 98754 - 98771
  • [37] Handling Imbalanced Data using Ensemble Learning in Software Defect Prediction
    Malhotra, Ruchika
    Jain, Juhi
    PROCEEDINGS OF THE CONFLUENCE 2020: 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING, 2020, : 300 - 304
  • [38] Hybrid deep architecture for software defect prediction with improved feature set
    Shyamala, C.
    Mohana, S.
    Ambika, M.
    Gomathi, K.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (31) : 76551 - 76586
  • [39] Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction
    Zhang, Qinhe
    Zhang, Jiachen
    Feng, Tie
    Xue, Jialang
    Zhu, Xinxin
    Zhu, Ningyang
    Li, Zhiheng
    IET SOFTWARE, 2024, 2024
  • [40] A random approximate reduct-based ensemble learning approach and its application in software defect prediction
    Jiang, Feng
    Yu, Xu
    Gong, Dunwei
    Du, Junwei
    Information Sciences, 2022, 609 : 1147 - 1168