Developing a reliable software system, several issues need to be
addressed. These issues include the definition of reliable
software, reliable development methodologies, testing methods for
reliability, and reliability growth prediction modeling. Many
software reliability growth models were proposed with the goal to
estimate the number of residual software faults, which occur in the
software testing process. In this thesis, we explore an alternative
approach using two types of neural networks (NN) models, the
feedforward and the Radial basis function. We also use of fuzzy
rules. NNs have been used both to estimate parameters of a formal
model and to learn to emulate the process model itself to predict
future faults. Feedforward and Radial basis function have been
successfully used to solve a variety of prediction problems, which
include real-time control, military, and operating system
applications. A set of fuzzy rules were also developed to model the
dynamics of the software reliability growth models in various
applications. The reported results using neural networks and fuzzy
logic can improve the software reliability growth modeling
solution.
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