Recalling the "imprecision" of cross-project defect prediction

2012 ◽  
Author(s):  
Foyzur Rahman ◽  
Daryl Posnett ◽  
Premkumar Devanbu
Keyword(s):  
Defect Prediction ◽  
Cross Project

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

An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems

2017 ◽  
Vol 43 (4) ◽  
pp. 321-339 ◽  
Author(s):  
Xiao-Yuan Jing ◽  
Fei Wu ◽  
Xiwei Dong ◽  
Baowen Xu
Keyword(s):  
Class Imbalance ◽  
Defect Prediction ◽  
Cross Project

Cross-project smell-based defect prediction

2021 ◽  
Author(s):  
Bruno Sotto-Mayor ◽  
Meir Kalech
Keyword(s):  
Defect Prediction ◽  
Cross Project

scholarly journals HDA: Cross-Project Defect Prediction via Heterogeneous Domain Adaptation With Dictionary Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57597-57613 ◽  
Author(s):  
Zhou Xu ◽  
Peipei Yuan ◽  
Tao Zhang ◽  
Yutian Tang ◽  
Shuai Li ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Domain Adaptation ◽  
Defect Prediction ◽  
Cross Project

An empirical study of just-in-time defect prediction using cross-project models

2014 ◽  
Author(s):  
Takafumi Fukushima ◽  
Yasutaka Kamei ◽  
Shane McIntosh ◽  
Kazuhiro Yamashita ◽  
Naoyasu Ubayashi
Keyword(s):  
Empirical Study ◽  
Defect Prediction ◽  
Just In Time ◽  
Cross Project

A Framework for Homogeneous Cross-Project Defect Prediction

2021 ◽  
Vol 9 (1) ◽  
pp. 52-68
Author(s):  
Lipika Goel ◽  
Mayank Sharma ◽  
Sunil Kumar Khatri ◽  
D. Damodaran
Keyword(s):  
Class Imbalance ◽  
The Other ◽  
Training Data ◽  
Defect Prediction ◽  
Rank Test ◽  
Ensemble Model ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Proposed Model ◽  
Cross Project

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.

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