Using Pattern Position Distribution for Software Failure Detection

DOI

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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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  - 10.1080/18756891.2013.768442
ID  - Li2013
ER  -