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

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Page 296

Specifically , they should indicate whether the

covered [ 16 ] , whether the test cases accurately simulate the operating

environment [ 17 ] and whether the test cases are adequate to detect all likely

errors [ 18 ] ...

Specifically , they should indicate whether the

**input**domain has been adequatelycovered [ 16 ] , whether the test cases accurately simulate the operating

environment [ 17 ] and whether the test cases are adequate to detect all likely

errors [ 18 ] ...

Page 326

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

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

...

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

**Inputs**are selected randomly and independently from the**input**domain accordingto the operational distribution . This is a very strong assumption and will not hold

...

Page 330

2 ) It does not take into account « continuity » in the

most real - time control systems , the successive

are sensor readings of physical quantities , like temperature , which cannot ...

2 ) It does not take into account « continuity » in the

**input**domain . ... Further , formost real - time control systems , the successive

**inputs**are correlated if the**inputs**are sensor readings of physical quantities , like temperature , which cannot ...

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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