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.

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.

Reliability Analysis and Modeling of Green Computing Based Software Systems

2020 ◽  
Vol 13 ◽  
Author(s):  
Sangeeta ◽  
Kapil Sharma ◽  
Manju Bala
Keyword(s):  
Software Development ◽  
Software Reliability ◽  
Gravitational Search Algorithm ◽  
Green Computing ◽  
Software Systems ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Hybrid Particle Swarm Optimization ◽  
Proposed Model ◽  
Green Software

Background: oftware industries are growing very fast to develop new solutions and ease people’s life. Software reliability has been considered as a critical factor in today’s growing digital world. Software reliability models are one of the most generally used mathematical tools for estimation of software reliability. These reliability models can be applied on development of sustainable and green computing-based software’s having their constrained development environments. Objective: This paper proposes a new reliability estimation model for green IT environment based software systems. Methods: In this paper, a new failure rate behavior-based model centered on green software development life cycle process has been developed. This model integrates a new modulation factor for incorporating changing needs in each phase of green software development methodology. Parameter estimation for proposed model has been done using hybrid Particle Swarm Optimization and Gravitational Search Algorithm. The proposed model has been tested on real-world datasets. Results: Experimental results are showing the enhanced capability of proposed model in simulating real green software development environment. Using GC-1 and GC-2 dataset, proposed model is about 60.05% more significant than other models.

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.

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.

Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)

2020 ◽  
Vol 8 (2) ◽  
pp. 129-140
Author(s):  
Sudharson D ◽  
Prabha Dr
Keyword(s):  
Ant Colony Optimization ◽  
Software Reliability ◽  
Ant Colony ◽  
Test Case ◽  
Content Type ◽  
Reliability Models ◽  
Efficient Management ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Development Cycle

PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.FindingsThe ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.Originality/valueConventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness.

SOFTWARE RELIABILITY MODELS WITH TWO DEBUGGING RATES

1999 ◽  
Vol 06 (01) ◽  
pp. 31-42 ◽  
Author(s):  
OLIVIER GAUDOIN
Keyword(s):  
Maximum Likelihood ◽  
Simple Model ◽  
Software Reliability ◽  
Failure Process ◽  
Real Data ◽  
Software Faults ◽  
Reliability Models ◽  
Software Failure ◽  
New Class ◽  
Software Reliability Models

A new class of software reliability models is proposed, based on practical considerations about the failure process and the influence of debugging on the successive software failure rates. It is assumed that, at each correction, a part of the previous software faults are removed and new faults can be introduced. Several well-known models belong to this class. With some additional assumptions, a simple model is proposed. The maximum likelihood equations for the estimation of its parameters are derived. The model is applied on real data and is compared to usual software reliability models.

scholarly journals The Flexible Weibull Extension-Burr XII Distribution: Model, Properties and Applications

2020 ◽  
pp. 447-460
Author(s):  
Rania M. Kamal ◽  
Moshira A. Ismail
Keyword(s):  
Additive Model ◽  
Real Data ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Distribution Model ◽  
Data Set ◽  
Burr Xii Distribution ◽  
Proposed Model ◽  
The Moment ◽  
Modified Weibull

This paper is devoted to study a new four- parameter additive model. The newly suggested model is referred to as the flexible Weibull extension-Burr XII distribution. It is derived by considering a serial system with one component following a flexible Weibull extension distribution and another following a Burr XII distribution. The usefulness of the model stems from the flexibility of its failure rate which accommodates bathtub and modified bathtub among other risk patterns. These two patterns have been widely accepted in several fields, especially reliability and engineering fields. In addition, the importance of the new distribution is that it includes new sub-models which are not known in the literature. Some statistical properties of the proposed distribution such as quantile function, the mode, the rth moment, the moment generating function and the order statistics are discussed. Moreover, the method of maximum likelihood is used to estimate the parameters of the model. Also, to evaluate the performance of the estimators, a simulation study is carried out. Finally, the performance of the proposed distribution is compared through a real data set to some well-known distributions including the new modified Weibull, the additive Burr and the additive Weibull distributions. It is shown that the proposed model provides the best fit for the used real data set.  

SOFTWARE FAILURE DATA ANALYSIS USING THE LEAST SQUARES APPROACH AND THE TIME PER FAILURE CONCEPT

1995 ◽  
Vol 02 (02) ◽  
pp. 161-176 ◽  
Author(s):  
G.J. KNAFL ◽  
J.A. MORGAN ◽  
R.L. FOLLENWEIDER ◽  
R.M. KARCICH
Keyword(s):  
Software Reliability ◽  
Evaluation Procedure ◽  
Data Set ◽  
Reliability Models ◽  
Mean Time Between Failures ◽  
Software Failure ◽  
Failure Data ◽  
Software Reliability Models ◽  
Power Models ◽  
Failure Intensity

We adapt data analytic techniques to the software reliability setting. We develop an evaluation procedure based on scatterplots of transformed data, crossvalidation using the predicted residual sum of squares (PRESS) criterion, residual plots, and normal plots. We analyze a software failure data set collected at Storage Technology Corporation utilizing this evaluation technique. We identify a new model which, for this data set, outperforms several established software reliability models, including the delayed S-shaped, exponential, inverse linear, logarithmic, power, and log power models. The failure intensity, and hence the reliability, for this model at any point in time is a function of the time per failure, that is, the ratio of cumulative time divided by cumulative failures, a quantity that agrees with the mean time between failures for time points at which failures occur.

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.

scholarly journals Analysis of the Chen’s and Pham’s Software Reliability Models

2018 ◽  
Vol 18 (3) ◽  
pp. 37-47
Author(s):  
Nikolay Pavlov ◽  
Anton Iliev ◽  
Asen Rahnev ◽  
Nikolay Kyurkchiev
Keyword(s):  
Software Reliability ◽  
Step Function ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Reliability Model ◽  
Heaviside Step Function ◽  
Reliability Models ◽  
The Best Approximation ◽  
Software Reliability Models ◽  
Software Reliability Model

Abstract In this paper we study the Hausdorff approximation of the shifted Heaviside step function ht0(t) by sigmoidal functions based on the Chen’s and Pham’s cumulative distribution functions and find an expression for the error of the best approximation. We give real examples with data provided by IBM entry software package and Apache HTTP Server using Chen’s software reliability model and Pham’s deterministic software reliability model. Some analyses are made.

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