A Three-Stage Defect Prediction Model for Cross-Project Defect Prediction

2017 ◽  
Author(s):  
Song Huang ◽  
Yaning Wu ◽  
Haijin Ji ◽  
Chengzu Bai
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Cross Project

scholarly journals Cross-Project Defect Prediction Method Based on Manifold Feature Transformation

2021 ◽  
Vol 13 (8) ◽  
pp. 216
Author(s):  
Yu Zhao ◽  
Yi Zhu ◽  
Qiao Yu ◽  
Xiaoying Chen
Keyword(s):  
Prediction Model ◽  
Data Distribution ◽  
Prediction Method ◽  
Feature Space ◽  
Defect Prediction ◽  
Software Project ◽  
Feature Transformation ◽  
Traditional Methods ◽  
The Difference ◽  
Cross Project

Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.

Research of Software Defect Prediction Model Based on ACO-SVM

2011 ◽  
Vol 34 (6) ◽  
pp. 1148-1154 ◽  
Author(s):  
Hui-Yan JIANG ◽  
Mao ZONG ◽  
Xiang-Ying LIU
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Model Based ◽  
Software Defect

Heterogeneous Cross Project Defect Prediction in Software

2020 ◽  
Author(s):  
Sonali Srivastava ◽  
Shikha Rani ◽  
Shailly Singh ◽  
Saurabh Singh ◽  
Rohit Vashisht
Keyword(s):  
Defect Prediction ◽  
Cross Project
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