원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.10 No.8
2015.08
pp.1-12
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Finding defects in a software system is not easy. Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects, which applies machine learning algorithms (classifiers) and defect distribution model. This approach includes two steps. Firstly, use machine learning Algorithms and Association Rules to get defect classification table, then confirm the defect distribution trend referring to several distribution models. Experiment results on a GUI project show that the approach can effectively improve the accuracy of defect prediction and be used for test planning and implementation.
목차
Abstract
1. Introduction
2. Related Classifier Models
2.1. Associations Rules
2.2. Decision Tree
2.3. K-Nearest Neighbour
3. Related Defect Distribution Model
3.1. Rayleigh Distribution Model
3.2. Exponential Distributed Model
3.3. S-curve Distributed Model
3.4. The Lognormal Distribution Model
3.5. Bayesian Belief Networks
4. Defect Classification using Classifiers
5. Defect Prediction using Distribution Model
6. Conclusions
Acknowledgements
References
1. Introduction
2. Related Classifier Models
2.1. Associations Rules
2.2. Decision Tree
2.3. K-Nearest Neighbour
3. Related Defect Distribution Model
3.1. Rayleigh Distribution Model
3.2. Exponential Distributed Model
3.3. S-curve Distributed Model
3.4. The Lognormal Distribution Model
3.5. Bayesian Belief Networks
4. Defect Classification using Classifiers
5. Defect Prediction using Distribution Model
6. Conclusions
Acknowledgements
References
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자료제공 : 네이버학술정보