Title :
Improving Classification Efficiency of Orthogonal Defect Classification via a Bayesian Network Approach
Author :
Wang He ; Wang Hao ; Lin Zhiqing
Author_Institution :
Pattern Recogniation & Intell. Syst. Lab., Beijing Univ. of Posts & Tele Commun., Beijing, China
Abstract :
Orthogonal defect classification (ODC) is a kind of defect analysis method invented by IBM. ODC classifies software defects by eight orthogonal attributes. By analyzing these attributes´ distribution and increasing trend the software process information could be obtained. It has been used widely in many companies and organizations. In this paper, we focus on the ODC records collected in a company, and research to use these data to provide guidance in actual defect management to improve the efficiency of the classification. We study the relationships of these attributes and give a Bayesian network model, then with the help of the ODC records we got, a Bayesian network for ODC is presented. It shows great help in actual work for both the developers and the testers.
Keywords :
belief networks; pattern classification; software management; software quality; Bayesian network approach; attribute distribution analysis; defect analysis method; defect management; orthogonal defect classification; software defect classification; software process information; software quality; Bayesian methods; Feedback; Helium; Information analysis; Intelligent networks; Intelligent systems; Pattern analysis; Pattern recognition; Software quality; Software testing;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5363694