An NHPP Software Reliability Model and Its Comparison

1997 ◽  
Vol 04 (03) ◽  
pp. 269-282 ◽  
Author(s):  
Hoang Pham ◽  
Xuemei Zhang
Keyword(s):  
Software Reliability ◽  
Visual Basic ◽  
Model Parameters ◽  
Data Sets ◽  
New Model ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Better Than

In this paper, software reliability models based on a nonhomogeneous Poisson process (NHPP) are summarized. A new model based on NHPP is presented. All models are applied to two widely used data sets. It can be shown that for the failure data used here, the new model fits and predicts much better than the existing models. A software program is written, using Excel & Visual Basic, which can be used to facilitate the task of obtaining the estimators of model parameters.

A SOFTWARE RELIABILITY MODEL FOR ARTIFICIAL INTELLIGENCE PROGRAMS

1993 ◽  
Vol 03 (01) ◽  
pp. 99-114 ◽  
Author(s):  
FAROKH B. BASTANI ◽  
ING-RAY CHEN ◽  
TA-WEI TSAO
Keyword(s):  
Artificial Intelligence ◽  
Software Reliability ◽  
Planning Problem ◽  
Reliability Growth ◽  
Reliability Model ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Correctness Criterion

In this paper we develop a software reliability model for Artificial Intelligence (AI) programs. We show that conventional software reliability models must be modified to incorporate certain special characteristics of AI programs, such as (1) failures due to intrinsic faults, e.g., limitations due to heuristics and other basic AI techniques, (2) fuzzy correctness criterion, i.e., difficulty in accurately classifying the output of some AI programs as correct or incorrect, (3) planning-time versus execution-time tradeoffs, and (4) reliability growth due to an evolving knowledge base. We illustrate the approach by modifying the Musa-Okumoto software reliability growth model to incorporate failures due to intrinsic faults and to accept fuzzy failure data. The utility of the model is exemplified with a robot path-planning problem.

scholarly journals Application of EM Algorithm to NHPP-Based Software Reliability Assessment with Generalized Failure Count Data

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 985
Author(s):  
Hiroyuki Okamura ◽  
Tadashi Dohi
Keyword(s):  
Em Algorithm ◽  
Software Reliability ◽  
Count Data ◽  
Failure Time ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Time Data ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models

Software reliability models (SRMs) are widely used for quantitative evaluation of software reliability by estimating model parameters from failure data observed in the testing phase. In particular, non-homogeneous Poisson process (NHPP)-based SRMs are the most popular because of their mathematical tractability. In this paper, we focus on the parameter estimation algorithm for NHPP-based SRMs and discuss the EM algorithm for generalized fault count data. The presented algorithm can be applied for failure time data, failure count data, and their mixture. The paper derives the EM-step formulas for basic 12 NHPP-based SRMs and demonstrate a numerical experiment to present the convergence property of our algorithms. The developed algorithms are suitable for an automatic tool for software reliability evaluation.

scholarly journals A New Flexible Version of the Lomax Distribution with Applications

2018 ◽  
Vol 7 (5) ◽  
pp. 120
Author(s):  
T. H. M. Abouelmagd
Keyword(s):  
Estimation Method ◽  
Good Alternative ◽  
Model Parameters ◽  
Data Sets ◽  
Suitable Model ◽  
Lifetime Data ◽  
New Model ◽  
The Right ◽  
New Distribution ◽  
Better Than

A new version of the Lomax model is introduced andstudied. The major justification for the practicality of the new model isbased on the wider use of the Lomax model. We are also motivated tointroduce the new model since the density of the new distribution exhibitsvarious important shapes such as the unimodal, the right skewed and the leftskewed. The new model can be viewed as a mixture of the exponentiated Lomaxdistribution. It can also be considered as a suitable model for fitting thesymmetric, left skewed, right skewed, and unimodal data sets. The maximumlikelihood estimation method is used to estimate the model parameters. Weprove empirically the importance and flexibility of the new model inmodeling two types of aircraft windshield lifetime data sets. The proposedlifetime model is much better than gamma Lomax, exponentiated Lomax, Lomaxand beta Lomax models so the new distribution is a good alternative to thesemodels in modeling aircraft windshield data.

scholarly journals A Software Reliability Model Using Quantile Function

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Bijamma Thomas ◽  
Midhu Narayanan Nellikkattu ◽  
Sankaran Godan Paduthol
Keyword(s):  
Software Reliability ◽  
Real Data ◽  
Quantile Function ◽  
Data Set ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Reliability Characteristics ◽  
Proposed Model ◽  
L Moments

We study a class of software reliability models using quantile function. Various distributional properties of the class of distributions are studied. We also discuss the reliability characteristics of the class of distributions. Inference procedures on parameters of the model based on L-moments are studied. We apply the proposed model to a real data set.

FEEDBACK CONTROL SYSTEM FOR SOFTWARE

2007 ◽  
Vol 14 (05) ◽  
pp. 411-429
Author(s):  
NORMAN SCHNEIDEWIND
Keyword(s):  
Feedback Control ◽  
Control Systems ◽  
Software Reliability ◽  
Space Shuttle ◽  
Physical Systems ◽  
Reliability Models ◽  
Software Failure ◽  
Failure Data ◽  
Software Reliability Models ◽  
Feedback Control Systems

Feedback control systems are used in many walks of life, including automobiles, airplanes, and nuclear reactors. These are all physical systems, albeit with a considerable does of software. It occurred to us that there is no reason that feedback control systems could not be applied to the software process, specifically dealing with reliability analysis, test, and prediction. Thus, we constructed a model of such a system and analyzed whether feedback control, in the form of error signals representing deviations from desired behavior, could bring observed behavior in conformance with specifications. To conduct the experiment, we used NASA Space Shuttle software failure data and analyzed the feedback when no faults were removed versus removing faults. In making this evaluation two software reliability models were used: the Musa Logarithmic Model and the Schneidewind Model. In general, feedback based on fault removal allowed the software reliability process to provide more accurate predictions and, hence, finer control over the process.

BOOTSTRAP CONFIDENCE INTERVALS OF SOFTWARE RELIABILITY MEASURES BASED ON A GAMMA FUNCTION MODEL

2008 ◽  
Vol 15 (01) ◽  
pp. 55-66
Author(s):  
MITSUHIRO KIMURA
Keyword(s):  
Confidence Intervals ◽  
Gamma Function ◽  
Software Reliability ◽  
Reliability Assessment ◽  
Numerical Differentiation ◽  
Model Parameters ◽  
Function Model ◽  
Data Set ◽  
Reliability Models ◽  
Software Reliability Models

This paper focuses on the generalization of several software reliability models and the derivation of confidence intervals of reliability assessment measures. First we propose a gamma function model as a generalized model, and discuss how to obtain the confidence intervals from a data set by using a bootstrap scheme when the size of the data set is small. A two-parameter numerical differentiation method is applied to the data set to estimate the model parameters. We also show several numerical illustrations of software reliability assessment.

Incorporating the Testing-Effort Function into the Inflected S-Shaped NHPP Software Reliability Model

2012 ◽  
Vol 241-244 ◽  
pp. 356-359
Author(s):  
Qian Zhao ◽  
Xian Feng Yu ◽  
Cheng Wei Zhang
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Accurate Estimation ◽  
Data Sets ◽  
Reliability Modeling ◽  
Testing Effort ◽  
Failure Data ◽  
Modeling Process ◽  
Software Reliability Model ◽  
Effort Function

Considering testing effort and imperfect debugging in reliability modeling process may further improve the fitting and prediction results of software reliability growth models (SRGMs). For describing the S-shaped varying trend of the testing-effort increasing rate more accurately, this paper first proposes a inflected S-shaped testing effort function (IS-TEF). Then this new TEF is incorporated into the inflected S-shaped NHPP SRGMs for obtaining a new NHPP SRGMs which consider S-shaped TEF (IS-TEFM-IS). Finally the IS-TEFM-IS and several comparison NHPP SRGMs are applied into two real failure data-sets respectively for investigating the fitting power of the IS-TEFM-IS. The experimental results show that the inflected S-shaped NHPP SRGM considering IS-TEF yields the best accurate estimation results than the other comparison SRGMs.

scholarly journals Generalized Software Reliability Model Considering Uncertainty and Dynamics: Model and Applications

2017 ◽  
Vol 27 (06) ◽  
pp. 967-993 ◽  
Author(s):  
Kiyoshi Honda ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa
Keyword(s):  
Stochastic Process ◽  
Software Reliability ◽  
Reliability Model ◽  
Dynamics Model ◽  
Development Environment ◽  
Team Members ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Software Reliability Model

Today’s development environment has changed drastically; the development periods are shorter than ever and the number of team members has increased. Consequently, controlling the activities and predicting when a development will end are difficult tasks. To adapt to changes, we propose a generalized software reliability model (GSRM) based on a stochastic process to simulate developments, which include uncertainties and dynamics such as unpredictable changes in the requirements and the number of team members. We assess two actual datasets using our formulated equations, which are related to three types of development uncertainties by employing simple approximations in GSRM. The results show that developments can be evaluated quantitatively. Additionally, a comparison of GSRM with existing software reliability models confirms that the approximation by GSRM is more precise than those by existing models.

scholarly journals The Two-Parameter Odd Lindley Weibull Lifetime Model with Properties and Applications

2018 ◽  
Vol 7 (4) ◽  
pp. 57 ◽  
Author(s):  
Jehhan. A. Almamy ◽  
Mohamed Ibrahim ◽  
M. S. Eliwa ◽  
Saeed Al-mualim ◽  
Haitham M. Yousof
Keyword(s):  
Maximum Likelihood ◽  
Real Data ◽  
Weibull Model ◽  
Good Alternative ◽  
Model Parameters ◽  
Data Sets ◽  
New Model ◽  
Lifetime Model ◽  
Two Parameter ◽  
Better Than

In this work, we study the two-parameter Odd Lindley Weibull lifetime model. This distribution is motivated by the wide use of the Weibull model in many applied areas and also for the fact that this new generalization provides more flexibility to analyze real data. The Odd Lindley Weibull density function can be written as a linear combination of the exponentiated Weibull densities. We derive explicit expressions for the ordinary and incomplete moments, moments of the (reversed) residual life, generating functions and order statistics. We discuss the maximum likelihood estimation of the model parameters. We assess the performance of the maximum likelihood estimators in terms of biases, variances, mean squared of errors by means of a simulation study. The usefulness of the new model is illustrated by means of two real data sets. The new model provides consistently better fits than other competitive models for these data sets. The Odd Lindley Weibull lifetime model is much better than \ Weibull, exponential Weibull, Kumaraswamy Weibull, beta Weibull, and the three parameters odd lindly Weibull with three parameters models so the Odd Lindley Weibull model is a good alternative to these models in modeling glass fibres data as well as the Odd Lindley Weibull model is much better than the Weibull, Lindley Weibull transmuted complementary Weibull geometric and beta Weibull models so it is a good alternative to these models in modeling time-to-failure data.

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