DOI

10.1080/00224065.2018.1545494

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.

Document Type

Post-print Article

Publication Date

1-2-2019

Publisher Statement

Copyright © 2019 Taylor & Francis.

DOI: 10.1080/00224065.2018.1545494

The definitive version is available at:

https://www.tandfonline.com/doi/full/10.1080/00224065.2018.1545494

Full citation:

Hsieh, Min-Hsiung, Shuen-Lin Jeng, and Paul Kvam. “Critical Fault-Detecting Time Evaluation in Software with Discrete Compound Poisson Models.” Journal of Quality Technology 51, no. 1 (January 2, 2019): 94–108. doi:10.1080/00224065.2018.1545494.

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