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SRGMs Based on Stochastic Differential Equations

Authors: Archana Kumar; Pramod Kumar Kapur;

SRGMs Based on Stochastic Differential Equations

Abstract

This paper presents a software reliability growth model based on type Stochastic Differential Equation. As the size of a software system is large, the number of faults detected during the testing phase becomes large ; the change of the number of faults, which are detected and removed through each debugging, becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, two new software reliability growth model based on type of stochastic differential equation has been proposed. In Software Reliability Growth Model 1 Stochastic Differential Equation based generalized Erlang model and in Software Reliability Growth Model 2 Stochastic Differential Equation based generalized Erlang model with logistic error detection function is being considered.

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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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