International Journal of Computational Intelligence Systems

Volume 6, Issue 2, March 2013, Pages 234 - 243

Using Pattern Position Distribution for Software Failure Detection

Authors
Chunping Li, Ziniu Chen, Hao Du, Hui Wang, George Wilkie, JuanC. Augusto, Jun Liu
Corresponding Author
Chunping Li
Received 9 April 2012, Accepted 12 September 2012, Available Online 1 March 2013.
DOI
https://doi.org/10.1080/18756891.2013.768442How to use a DOI?
Keywords
Sequential Patterns, Classification Algorithm, Software Failure, Anomaly Detection
Abstract

We present a novel approach for using the pattern position distribution as features to detect software failure. In this approach, we divide an execution sequence into several sections and compute the pattern distribution in each section. The distribution of all patterns is then used as features to train a classifier. This approach outperforms conventional frequency based methods by more effectively identifying software failures occurring through misused software patterns. Comparative experiments show the effectiveness of our approach.

Copyright
© 2017, 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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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 2
Pages
234 - 243
Publication Date
2013/03/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.768442How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

TY  - JOUR
AU  - Chunping Li
AU  - Ziniu Chen
AU  - Hao Du
AU  - Hui Wang
AU  - George Wilkie
AU  - JuanC. Augusto
AU  - Jun Liu
PY  - 2013
DA  - 2013/03/01
TI  - Using Pattern Position Distribution for Software Failure Detection
JO  - International Journal of Computational Intelligence Systems
SP  - 234
EP  - 243
VL  - 6
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.768442
DO  - https://doi.org/10.1080/18756891.2013.768442
ID  - Li2013
ER  -