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.
Recommended 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. https://doi.org/10.1080/00224065.2018.1545494.