## Proceedings of the International School of Physics "Enrico Fermi", Volume 94 |

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Page 296

Almost all software reliability models assume that the test environment is identical

to the

cases that require a high confidence in the reliability estimate . 4 ) Correction ...

Almost all software reliability models assume that the test environment is identical

to the

**operational**environment . This limits the applicability of these models incases that require a high confidence in the reliability estimate . 4 ) Correction ...

Page 326

The major assumption of all software reliability growth models is : Assumption .

Inputs are selected randomly and independently from the input domain according

to the

...

The major assumption of all software reliability growth models is : Assumption .

Inputs are selected randomly and independently from the input domain according

to the

**operational**distribution . This is a very strong assumption and will not hold...

Page 327

... ( 8 ) ) = severity of testing process relative to

s ) ) < 0 . Remarks . 1 ) As we have noted above , f ( T ; ( : ) ) is the severity of the

testing process relative to the

...

... ( 8 ) ) = severity of testing process relative to

**operational**distribution ; 0 < f ( T ; (s ) ) < 0 . Remarks . 1 ) As we have noted above , f ( T ; ( : ) ) is the severity of the

testing process relative to the

**operational**distribution , where the testing severity...

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### Contents

STATISTICAL THEORY OF RELIABLITY | 8 |

Definitions and characterizations | 12 |

J KEILSON Stochastic models in reliability theory | 23 |

Copyright | |

37 other sections not shown

### Common terms and phrases

analysis application approach associated assume assumption BARLOW Bayesian calculation called complex components consider constant continuous correctness Course defined density depends derived described detected determine discussed distribution edited epochs equations equivalence ergodic errors estimate example exists expected exponential fact fail failure rate fault function given Hence important increasing independent input integration interest interval known likelihood limit Markov matrix mean measure method modules normal Note observed obtain occur operational parameters performance phase positive possible posterior predictive prior probability problem procedure prove random variables renewal repair requirements rule sample selected sequence simple software reliability space specification statistical stochastic structure Suppose task theorem theory tion transition tree University values York