Just-in-time defect prediction for mobile applications: using shallow or deep learning?

被引:5
作者
van Dinter, Raymon [1 ,2 ]
Catal, Cagatay [3 ]
Giray, Goerkem
Tekinerdogan, Bedir [1 ]
机构
[1] Wageningen Univ & Res, Informat Technol Grp, Wageningen, Netherlands
[2] Sioux Technol, Apeldoorn, Netherlands
[3] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar
关键词
Just-in-time defect prediction; Shallow learning; XGBoost; Deep learning; Imbalanced learning; SOFTWARE; FRAMEWORK; MODELS;
D O I
10.1007/s11219-023-09629-1
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.
引用
收藏
页码:1281 / 1302
页数:22
相关论文
共 50 条
  • [31] A Preliminary Evaluation of CPDP Approaches on Just-in-Time Software Defect Prediction
    Amasaki, Sousuke
    Aman, Hirohisa
    Yokogawa, Tomoyuki
    [J]. 2021 47TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2021), 2021, : 279 - 286
  • [32] Deep Learning-Based Defect Prediction for Mobile Applications
    Jorayeva, Manzura
    Akbulut, Akhan
    Catal, Cagatay
    Mishra, Alok
    [J]. SENSORS, 2022, 22 (13)
  • [33] Improve cross-project just-in-time defect prediction with dynamic transfer learning
    Dai, Hongming
    Xi, Jianqing
    Dai, Hong-Liang
    [J]. JOURNAL OF SYSTEMS AND SOFTWARE, 2025, 219
  • [34] Effort-Aware Just-in-Time Bug Prediction for Mobile Apps Via Cross-Triplet Deep Feature Embedding
    Xu, Zhou
    Zhao, Kunsong
    Zhang, Tao
    Fu, Chunlei
    Yan, Meng
    Xie, Zhiwen
    Zhang, Xiaohong
    Catolino, Gemma
    [J]. IEEE TRANSACTIONS ON RELIABILITY, 2022, 71 (01) : 204 - 220
  • [35] Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks
    Ardimento, Pasquale
    Aversano, Lerina
    Bernardi, Mario Luca
    Cimitile, Marta
    Iammarino, Martina
    [J]. PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES (ICSOFT), 2021, : 310 - 317
  • [36] Towards Reliable Online Just-in-Time Software Defect Prediction
    Cabral, George G.
    Minku, Leandro L.
    [J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2023, 49 (03) : 1342 - 1358
  • [37] A Practical Human Labeling Method for Online Just-in-Time Software Defect Prediction
    Song, Liyan
    Minku, Leandro Lei
    Teng, Cong
    Yao, Xin
    [J]. PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, 2023, : 605 - 617
  • [38] Effort-Aware semi-Supervised just-in-Time defect prediction
    Li, Weiwei
    Zhang, Wenzhou
    Jia, Xiuyi
    Huang, Zhiqiu
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2020, 126
  • [39] A Formal Explainer for Just-In-Time Defect Predictions
    Yu, Jinqiang
    Fu, Michael
    Ignatiev, Alexey
    Tantithamthavorn, Chakkrit
    Stuckey, Peter
    [J]. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2024, 33 (07)
  • [40] Effort-aware cross-project just-in-time defect prediction framework for mobile apps
    Cheng, Tian
    Zhao, Kunsong
    Sun, Song
    Mateen, Muhammad
    Wen, Junhao
    [J]. FRONTIERS OF COMPUTER SCIENCE, 2022, 16 (06)