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Fuzzy Methods in Software Reliability Modeling

  • Kai-Yuan Cai
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 363)

Abstract

Software is developed by human. If human commits errors, defects may be introduced into software. Under some circumstances, defects may be activated by software inputs and lead to faulty software states and eventually result in software failures. This arouses software reliability problems. Software reliability modeling is aimed at using mathematical tools to deal with software reliability problems. By continuous-time software reliability modeling we mean that the time base used to measure software reliability is continuous, such as calendar time, clock time and CPU execution time. There have been many methodologies adopted for software reliability modeling [2, 13], including random-time methodology, by which inter-failure times are treated as random variables; stochastic methodology, by which the number of software failures occurring in a time interval is treated as a stochastic process; Bayesian methodology; fuzzy methodology; neural network methodology; non-parametric methodology; and others. In this section we introduce a fuzzy software reliability growth model which is applicable to software testing phase in which software reliability normally demonstrates growth trends [4].

Keywords

Fuzzy Variable Software Reliability Fuzzy Method Software Failure Possibility Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Kai-Yuan Cai
    • 1
  1. 1.Department of Automatic ControlBeijing University of Aeronautics and AstronauticsBeijingChina

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