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The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments

Salako, K. ORCID: 0000-0003-0394-7833 & Zhao, X. (2023). The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments. IEEE Transactions on Software Engineering, doi: 10.1109/TSE.2022.3233802


When assessing a software-based system, the results of Bayesian statistical inference on operational testing data can provide strong support for software reliability claims. For inference, this data (i.e. software successes and failures) is often assumed to arise in an independent, identically distributed (i.i.d.) manner. In this paper we show how conservative Bayesian approaches make this assumption unnecessary, by incorporating one’s doubts about the assumption into the assessment. We derive conservative confidence bounds on a system’s probability of failure on demand (pfd), when operational testing reveals no failures. The generality and utility of the confidence bounds are illustrated in the assessment of a nuclear power-plant safety-protection system, under varying levels of skepticism about the i.i.d. assumption. The analysis suggests that the i.i.d. assumption can make Bayesian reliability assessments extremely optimistic – such assessments do not explicitly account for how software can be very likely to exhibit no failures during extensive operational testing despite the software’s pfd being undesirably large.

Publication Type: Article
Additional Information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: conservative Bayesian inference, CBI, dependability claims, independent software failures, operational testing, software reliability assessment, statistical testing
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > Software Reliability
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