Earticle

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

키워드

저자

• WanJiang Han [ School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China ]
• LiXin Jiang [ Department of Emergency Response, China Earthquake Networks Center, Beijing 100036, China ]
• TianBo Lu [ School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China ]
• XiaoYan Zhang [ School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605