SDE Based SRGM Considering Irregular Fluctuation in Fault Introduction Rate

Deepika (), Adarsh Anand (), Shinji Inoue () and Prashant Johri ()
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Deepika: University of Delhi
Adarsh Anand: University of Delhi
Shinji Inoue: Kansai University
Prashant Johri: Galgotias University

A chapter in Predictive Analytics in System Reliability, 2023, pp 67-80 from Springer

Abstract: Abstract Software debugging is complicated and can be considered as stochastic in nature. During fault removal, debuggers at-times introduce new faults. Thereafter, the fault introduction process can be said to be non-linear in nature. In this study, we have proposed a software reliability growth model considering the irregular fluctuation of fault introduction rate over time with non-constant fault detection rate. We assume that fault introduction changes non-linearly over time and the fault introduction rate fluctuates irregularly. Ito’s process is used for solving the differential equation to find the analytical solution. The model is fitted on two real world data sets from two open-source project: Mozilla and Gnome. The experimental findings show that present model exhibit estimation result and having strong prediction skill.

Keywords: Brownian motion; Irregular fluctuation; Ito’s integral; Fault removal; Statistical analytical software (SAS); Stochastic differential equation (SDE); Wiener process (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-05347-4_5

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DOI: 10.1007/978-3-031-05347-4_5

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