Stochastic differential equation-based flexible software reliability growth model

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Kapur, P.K.
Anand, Sameer
Yamada, Shigeru
Yadavalli, Venkata S. Sarma

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Hindawi

Abstract

Several software reliability growth models (SRGMs) have been developed by software developers in tracking and measuring the growth of reliability. As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that 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, we propose a new software reliability growth model based on Itô type of stochastic differential equation. We consider an SDE-based generalized Erlang model with logistic error detection function. The model is estimated and validated on real-life data sets cited in literature to show its flexibility. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP-based models.

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Keywords

Maximum likelihood, Data sets, Coefficient of multiple determination, Statistical package for social sciences, Mean square error, Prediction error, Root mean square prediction error, Fault detection rate, Software Reliability Growth Models (SRGMs), Prediction fault content, Reliability of software, Flexible SRGMs

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Citation

Kapur, PK, Anand, S, Yamada, S & Yadavalli VSS 2009, 'Stochastic differential equation based flexible software reliability growth model', Mathematical Problems in Engineering, vol. 2009. [http://www.hindawi.com/journals/mpe/]