DocumentCode :
841276
Title :
Better reliability assessment and prediction through data clustering
Author :
Tian, Jeff
Author_Institution :
Dept. of Comput. Sci. & Eng., Southern Methodist Univ., Dallas, TX, USA
Volume :
28
Issue :
10
fYear :
2002
fDate :
10/1/2002 12:00:00 AM
Firstpage :
997
Lastpage :
1007
Abstract :
This paper presents a new approach to software reliability modeling by grouping data into clusters of homogeneous failure intensities. This series of data clusters associated with different time segments can be directly used as a piecewise linear model for reliability assessment and problem identification, which can produce meaningful results early in the testing process. The dual model fits traditional software reliability growth models (SRGMs) to these grouped data to provide long-term reliability assessments and predictions. These models were evaluated in the testing of two large software systems from IBM. Compared with existing SRGMs fitted to raw data, our models are generally more stable over time and produce more consistent and accurate reliability assessments and predictions.
Keywords :
failure analysis; reliability theory; software reliability; statistical analysis; data cluster based reliability models; data clustering; data grouping; identification; input domain reliability models; piecewise linear model; reliability assessment; software reliability growth models; Data analysis; Failure analysis; Fluctuations; Helium; Piecewise linear techniques; Predictive models; Software reliability; Software systems; Software testing; System testing;
fLanguage :
English
Journal_Title :
Software Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0098-5589
Type :
jour
DOI :
10.1109/TSE.2002.1041055
Filename :
1041055
Link To Document :
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