An Approach for Analyzing Infrequent Software Faults Based on Outlier Detection

The fault analysis is critical process in software security system. However, identifying outliers in software faults has not been well addressed. In this paper, we define WCFPOF (weighted closed frequent pattern outlier factor) to measure the complete transactions, and propose a novel approach for d...

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Bibliographic Details
Published in2009 International Conference on Artificial Intelligence and Computational Intelligence Vol. 4; pp. 302 - 306
Main Authors Jiadong Ren, Qunhui Wu, Changzhen Hu, Kunsheng Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Cities and towns
Closed frequent pattern
Data mining
Fault analysis
Fault detection
Fault diagnosis
Information analysis
Information security
Itemsets
Outlier detection
Pattern analysis
Software systems
Transaction databases
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Summary:The fault analysis is critical process in software security system. However, identifying outliers in software faults has not been well addressed. In this paper, we define WCFPOF (weighted closed frequent pattern outlier factor) to measure the complete transactions, and propose a novel approach for detecting closed frequent pattern based outliers. Through discovering and maintaining closed frequent patterns, the outlier measure of each transaction is computed to generate outliers. The outliers are the data that contain relatively less closed frequent itemsets. To describe the reasons why detected outlier transactions are infrequent, the contradictive closed frequent patterns for each outlier are figured out. Experimental results show that our algorithm has shorter time consumption and better scalability.
ISBN:1424438357
9781424438358
DOI:10.1109/AICI.2009.345