JSM 2004 - Toronto

Abstract #301999

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Activity Number: 266
Type: Topic Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301999
Title: Stopping Rule for Software Testing Based on a Binary Segmentation Procedure
Author(s): Tae Yang*+ and Lynn Kuo
Companies: Myongji University and University of Connecticut
Address: Dept. of Mathematics, Yongin, Kyonggi, 449-728, Korea
Keywords: binary segmentation procedure ; deviance criterion ; reliability growth pattern ; stopping rule
Abstract:

We develop a method that can be used to evaluate the reliability of software and to determine when to stop testing. In software testing, the time-domain software reliability growth models are assumed to make dynamic changes at each failure and the successive failure rates may be relatively more homogeneous for a series of related tests. The homogeneous clustering of these rates can be carried out using a binary segmentation procedure. The Bayes estimate of a clustered failure rate provides a direct reliability assessment for these clusters. We introduce the notion of reliability growth based on these clusters, where software reliability growth can be explained by the gradual reduction of the clustered growth rates from earlier to later period with perturbations. Stopping rules on software testing are developed based on the clustered reliability growth model.

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