Software Defect Prediction Based on As-sociation Rule Classification

DOI

10.2991/icebi.2010.7How to use a DOI?

Keywords

Software defect prediction, association rule classification, CBA2, AUC

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.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Baojun Ma
AU  - Karel Dejaeger
AU  - Jan Vanthienen
AU  - Bart Baesens
PY  - 2010/12
DA  - 2010/12
TI  - Software Defect Prediction Based on As-sociation Rule Classification
BT  - Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI 2010)
PB  - Atlantis Press
SP  - 44
EP  - 50
SN  - 1951-6851
UR  - https://doi.org/10.2991/icebi.2010.7
DO  - 10.2991/icebi.2010.7
ID  - Ma2010/12
ER  -