Applying reliability models more effectively (software)

Lyu, Michael R.; Nikora, Allen P.

doi:10.1109/52.143104

Cited by 92 publications

(37 citation statements)

References 2 publications

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“…More detailed discussion of other techniques of model building involving different machine learning algorithms can be found in [19]. Lyu and Nikora [20] used linear combinations of models in order to improve the accuracy of reliability predictions. Model calibration is a very old practice used mostly for statistical models to tune parameters and make models more appropriate to a new environment.…”

Section: Main Problems Of Building Software Quality Prediction Modelsmentioning

confidence: 99%

A novel composite model approach to improve software quality prediction

Bouktif

Ahmed

Khalil

et al. 2010

Information and Software Technology

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Section: Main Problems Of Building Software Quality Prediction Modelsmentioning

confidence: 99%

A novel composite model approach to improve software quality prediction

Bouktif

Ahmed

Khalil

et al. 2010

Information and Software Technology

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“…The difference between the simulated data m(t k ) and the cumulative number of detected faults m k is measured by the mean square error (cf. Lyu and Nikora [8])…”

Section: Mean Square Errormentioning

confidence: 99%

Cost optimization of a software reliability growth model with imperfect debugging and a fault reduction factor

Manjula

Jain²,

Gulati³

2014

ANZIAMJ

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In modern society people depend on both hardware and software systems. A software system is embedded in every activity of a computer system. The desired performance of a software system is an important issue for many critical systems. Over the past decades, many software models were proposed for estimating the growth of reliability. To improve software quality, software reliability growth models (srgm) play an important role. The present investigation deals with a srgm with imperfect debugging, change points and a fault reduction factor (frf). A frf is the net number of faults removed in proportion to the failures experienced. This article proposes a new scheme for constructing a srgm based on a non-homogeneous Poisson process by

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“…The statistic evaluates the absolute difference between F * (t k ) and F(t k ) for each time point, and chooses the maximum value. (5) The Mean Square Error (MSE) is defined as (Lyu and Nikora, 1992):…”

Section: Criteria For Comparisonsmentioning

confidence: 99%