
doi: 10.2307/1403763
Summary: Probability models and statistical methods are a popular technique for evaluating the reliability of computer software. This paper reviews the literature concerning these methods, with an emphasis on the historical perspective. The use of stochastic techniques is justified, and the various probability models that have been proposed, along with any associated statistical estimation and inference procedure, are described. Examples of the models applied to real software failure data are given. A classic software development problem -- how long software should be tested before it is released into the marketplace -- is analyzed from a decision theoretic standpoint. Finally, the direction of future research is contemplated.
Reliability and life testing, historical perspective, review, de-eutrophication, real software failure data, Theory of software, utility, non-homogeneous Poisson process, mean value function, decision theory, autoregressive process, maximum likelihood, failure rate
Reliability and life testing, historical perspective, review, de-eutrophication, real software failure data, Theory of software, utility, non-homogeneous Poisson process, mean value function, decision theory, autoregressive process, maximum likelihood, failure rate
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 69 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
