The impact of feature reduction techniques on defect prediction models

2019 ◽  
Vol 24 (4) ◽  
pp. 1925-1963 ◽  
Author(s):  
Masanari Kondo ◽  
Cor-Paul Bezemer ◽  
Yasutaka Kamei ◽  
Ahmed E. Hassan ◽  
Osamu Mizuno
Keyword(s):  
Prediction Models ◽  
Feature Reduction ◽  
Defect Prediction ◽  
Reduction Techniques ◽  
Defect Prediction Models ◽  
The Impact

scholarly journals The Impact of Automated Parameter Optimization on Defect Prediction Models

2019 ◽  
Vol 45 (7) ◽  
pp. 683-711 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E. Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Parameter Optimization ◽  
Prediction Models ◽  
Defect Prediction ◽  
Defect Prediction Models ◽  
The Impact

scholarly journals The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models

2020 ◽  
Vol 46 (11) ◽  
pp. 1200-1219 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Ahmed E. Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Models ◽  
Defect Prediction ◽  
Defect Prediction Models ◽  
The Impact

The impact of tangled code changes on defect prediction models

2015 ◽  
Vol 21 (2) ◽  
pp. 303-336 ◽  
Author(s):  
Kim Herzig ◽  
Sascha Just ◽  
Andreas Zeller
Keyword(s):  
Prediction Models ◽  
Defect Prediction ◽  
Code Changes ◽  
Defect Prediction Models ◽  
The Impact

scholarly journals The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects

2022 ◽  
Vol 31 (1) ◽  
pp. 1-26
Author(s):  
Davide Falessi ◽  
Aalok Ahluwalia ◽  
Massimiliano DI Penta
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Defect Prediction ◽  
Training Set ◽  
Negative Effect ◽  
History Of ◽  
The Subject ◽  
Defect Prediction Models ◽  
The Impact ◽  
Substantial Effort

Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.

scholarly journals The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E. Hassan ◽  
Akinori Ihara ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Models ◽  
Defect Prediction ◽  
Defect Prediction Models ◽  
The Impact

scholarly journals On the time-based conclusion stability of cross-project defect prediction models

2020 ◽  
Vol 25 (6) ◽  
pp. 5047-5083
Author(s):  
Abdul Ali Bangash ◽  
Hareem Sahar ◽  
Abram Hindle ◽  
Karim Ali
Keyword(s):  
Prediction Models ◽  
Defect Prediction ◽  
Defect Prediction Models ◽  
Cross Project
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