title: |
Software Defect Prediction Based on As-sociation Rule Classification |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-40-6 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/icebi.2010.7 (how to use a DOI) | |
author(s): |
Baojun Ma, Karel Dejaeger, Jan Vanthienen, Bart Baesens |
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publication date: |
December 2010 |
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keywords: |
Software defect prediction,
association rule classification, CBA2,
AUC |
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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. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |