Estimating the probability of failure when testing reveals no failures

1 January 1992

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Software Engineering

Vol. 18 (1) , 33-43
https://doi.org/10.1109/32.120314

Abstract

Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution; and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.<>

Keywords

This publication has 18 references indexed in Scilit:

Reliability analysis of large software systems: defect data modeling
IEEE Transactions on Software Engineering, 1990
Software Reuse through Building Blocks
IEEE Software, 1987
An Evaluation of Random Testing
IEEE Transactions on Software Engineering, 1984
Software Reliability—Status and Perspectives
IEEE Transactions on Software Engineering, 1982
A User-Oriented Software Reliability Model
IEEE Transactions on Software Engineering, 1980
An Analysis of Competing Software Reliability Models
IEEE Transactions on Software Engineering, 1978
Testing for software reliability
Published by Association for Computing Machinery (ACM) ,1975
The new math of computer programming
Communications of the ACM, 1975
A Bayesian Reliability Model with a Stochastically Monotone Failure Rate
IEEE Transactions on Reliability, 1974
Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability
Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 1937