2010 International Conference on E-Business Intelligence, Date: 2010/12/19 - 2010/12/21, Location: PEOPLES R CHINA, Yunnan Univ Finance & Econom, Kunming
FBE Research Report KBI_1105
Author:
Keywords:
Software defect prediction, Association rule classification, CBA2, AUC, Social Sciences, Business, Business & Economics, association rule classification
Abstract:
In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, association based classification methods have not been investigated so far in this context. This paper assesses the use of such a classification method, CBA2, and compares it to other rule based classification methods. Furthermore, we investigate whether rule sets generated on data from one software project can be used to predict defective software modules in other, similar software projects. It is found that applying the CBA2 algorithm results in both accurate and comprehensible rule sets.