TheLp-norm estimation of the parameters for the Jelinski–Moranda model in software reliability

Jukić, Dragan

doi:10.1080/00207160.2011.642299

Cited by 4 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, ML has some inherent defects and can only be applied to detection and correction data with an equal time interval. In the Jelinski–Moranda model, which is the earliest GOS model, the ML estimate does not necessarily exist, and it is not necessarily unique . The ordinary least‐square (OLS) estimation method is a more common method to estimate parameters due to its simplicity.…”

Section: Introductionmentioning

confidence: 99%

A general modeling and analysis framework for software fault detection and correction process

Wang

et al. 2016

Software Testing Verif & Rel

View full text Add to dashboard Cite

Summary Software reliability growth modeling plays an important role in software reliability evaluation. To incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes has attracted widespread research attention recently. In modeling software correction processes, the assumption of fault correction time is relaxed from constant delay to random delay. However, stochastic distribution of fault correction time brings more difficulties in modeling and corresponding parameter estimation. In this paper, a framework of software reliability models containing both information from software fault detection process and correction process is studied. Different from previous extensions on software reliability growth modeling, the proposed approach is based on Markov model other than a nonhomogeneous Poisson process model. Also, parameter estimation is carried out with weighted least‐square estimation method, which emphasizes the influence of later data on the prediction. Two data sets from practical software development projects are applied with the proposed framework, which shows satisfactory performance with the results. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Section: Introductionmentioning

confidence: 99%