Critical fault-detecting time evaluation in software with discrete compound Poisson models

Abstract

Software developers predict their product’s failure rate using reliability growth models that are typically based on nonhomogeneous Poisson (NHP) processes. In this article, we extend that practice to a nonhomogeneous discrete-compound Poisson process that allows for multiple faults of a system at the same time point. Along with traditional reliability metrics such as average number of failures in a time interval, we propose an alternative reliability index called critical fault-detecting time in order to provide more information for software managers making software quality evaluation and critical market policy decisions. We illustrate the significant potential for improved analysis using wireless failure data as well as simulated data.

Keywords:

• goodness of fit
• maximum likelihood
• nonhomogeneous compound Poisson process
• l-fold convolution
• reliability growth model

Additional information

Notes on contributors

Min-Hsiung Hsieh

Dr. Hsieh is a Postdoctor in the Department of Statistics. His email address is [email protected].

Shuen-Lin Jeng

Dr. Jeng is an Associate Professor in the Department of Statistics. His email address is [email protected].

Paul Kvam

Dr. Kvam is a Professor in the Department of Mathematics & Computer Science. His email is [email protected].