.png){kind=icon}
返回
Commit-time defect prediction using one-class classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2023.111914.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing Just-In-Time Software Defect Prediction methods suffer from the data imbalance problem, where the majority class (normal commits) significantly outnumbers the minority class (buggy commits). This results in higher probability of misclassification. Various data balancing techniques have been proposed to address this challenge with varying degrees of success. In this study, we propose an approach that rely on One-Class Classification (OCC) to train models using data from the majority class only. This eliminates the need for data balancing. We compare the accuracy of three OCC algorithms -One-class SVM, Isolation Forest, and One-class k-NN -to their binary counterparts -SVM, Random Forest, and k-NN -on 34 software projects. Our results show that the data imbalance ratio (the proportion of normal to buggy commits) plays a crucial role in determining the optimal classification approach. We found that for projects with medium to high imbalance ratio, OCC algorithms outperform binary classifiers with and without data balancing, using cross and time -sensitive validation approaches. Furthermore, we found that OCC methods require fewer features for projects with medium to high IR, reducing the computational overhead of training and response time while providing better understanding of the data and algorithm behavior.
Keyword:
Just-In-Time Software Defect Prediction
(JIT-SDP)
One-class classification
Machine learning
Software maintenance and evolution
Software reliability
期刊
IF:
4.1
论文数:
5.4K
被引数:
8.4K
机构
引用论文
分享收藏
On hyperparameter optimization of machine learning algorithms: Theory and practice机器学习算法的超参数优化: 理论与实践.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation在二元分类评估中，马修斯相关系数 (MCC) 相对于F1得分和准确性的优势BMC GENOMICSOAAI
BMC GENOMICSIF3.7
OAAI分享收藏
查看更多内容