scholarly journals Software Reliability Model with Dependent Failures and SPRT

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1366
Author(s):  
Da Hye Lee ◽  
In Hong Chang ◽  
Hoang Pham
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Hypothesis Test ◽  
Evaluation Criteria ◽  
Ratio Test ◽  
Software Failures ◽  
Dependent Failures ◽  
Proposed Model ◽  
Real World Datasets

Software reliability and quality are crucial in several fields. Related studies have focused on software reliability growth models (SRGMs). Herein, we propose a new SRGM that assumes interdependent software failures. We conduct experiments on real-world datasets to compare the goodness-of-fit of the proposed model with the results of previous nonhomogeneous Poisson process SRGMs using several evaluation criteria. In addition, we determine software reliability using Wald’s sequential probability ratio test (SPRT), which is more efficient than the classical hypothesis test (the latter requires substantially more data and time because the test is performed only after data collection is completed). The experimental results demonstrate the superiority of the proposed model and the effectiveness of the SPRT.

scholarly journals A Software Reliability Model Considering the Syntax Error in Uncertainty Environment, Optimal Release Time, and Sensitivity Analysis

2018 ◽  
Vol 8 (9) ◽  
pp. 1483 ◽  
Author(s):  
Da Lee ◽  
In Chang ◽  
Hoang Pham ◽  
Kwang Song
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Economic Losses ◽  
Release Time ◽  
Reliability Model ◽  
Software Failures ◽  
Software Products ◽  
Software Reliability Model ◽  
Uncertainty Environment

The goal set by software developers is to develop high quality and reliable software products. During the past decades, software has become complex, and thus, it is difficult to develop stable software products. Software failures often cause serious social or economic losses, and therefore, software reliability is considered important. Software reliability growth models (SRGMs) have been used to estimate software reliability. In this work, we introduce a new software reliability model and compare it with several non-homogeneous Poisson process (NHPP) models. In addition, we compare the goodness of fit for existing SRGMs using actual data sets based on eight criteria. The results allow us to determine which model is optimal.

DEBUGGING PROCESS-ORIENTED DISCRETE SOFTWARE RELIABILITY MODELING

2009 ◽  
Vol 16 (04) ◽  
pp. 357-370 ◽  
Author(s):  
SHINJI INOUE ◽  
NAOKI IWAMOTO ◽  
SHIGERU YAMADA
Keyword(s):  
Discrete Time ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Reliability Growth ◽  
Reliability Modeling ◽  
New Approach ◽  
Time Modeling ◽  
Software Reliability Growth ◽  
Discrete Time Modeling

This paper discusses an new approach for discrete-time software reliability growth modeling based on an discrete-time infinite server queueing model, which describes a debugging process in a testing phase. Our approach enables us to develop discrete-time software reliability growth models (SRGMs) which could not be developed under conventional discrete-time modeling approaches. This paper also discuss goodness-of-fit comparisons of our discrete-time SRGMs with conventional continuous-time SRGMs in terms of the criterion of the mean squared errors, and show numerical examples for software reliability analysis of our models by using actual data.

scholarly journals A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 450 ◽  
Author(s):  
Kwang Yoon Song ◽  
In Hong Chang ◽  
Hoang Pham
Keyword(s):  
Sensitivity Analysis ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Mean Value ◽  
Operating Environment ◽  
Model Based ◽  
Proposed Model ◽  
Coverage Model ◽  
The Impact ◽  
Testing Coverage

We have been attempting to evaluate software quality and improve its reliability. Therefore, research on a software reliability model was part of the effort. Currently, software is used in various fields and environments; hence, one must provide quantitative confidence standards when using software. Therefore, we consider the testing coverage and uncertainty or randomness of an operating environment. In this paper, we propose a new testing coverage model based on NHPP software reliability with the uncertainty of operating environments, and we provide a sensitivity analysis to study the impact of each parameter of the proposed model. We examine the goodness-of-fit of a new testing coverage model based on NHPP software reliability and other existing models based on two datasets. The comparative results for the goodness-of-fit show that the proposed model does significantly better than the existing models. In addition, the results for the sensitivity analysis show that the parameters of the proposed model affect the mean value function.

A FUZZY SOFTWARE RELIABILITY MODEL WITH MULTIPLE-ERROR INTRODUCTION AND REMOVAL

2002 ◽  
Vol 09 (03) ◽  
pp. 215-227 ◽  
Author(s):  
LEV V. UTKIN ◽  
SERGEY V. GUROV ◽  
MAXIM I. SHUBINSKY
Keyword(s):  
Software Reliability ◽  
Efficient Algorithm ◽  
Reliability Model ◽  
Time Intervals ◽  
Numerical Examples ◽  
Fuzzy Variables ◽  
Software Failures ◽  
Software Reliability Model ◽  
Proposed Model ◽  
Multiple Error

A fuzzy software reliability model is proposed where the time intervals between the software failures are taken as the fuzzy variables governed by a membership function. The model takes into account the following assumptions: new faults may be introduced into the software during debugging processes, the number of faults removed after a failure may be greater than one, and there is a growth of human experience during debugging. The model can be considered as an extension of the model developed by Cai, Wen and Zhang. An efficient algorithm is presented for estimating parameters of the model. The numerical examples validate the proposed model.

scholarly journals A Unique Procedure for Accurate Recognition of Software Failures: A Software Reliability Growth Model Based on Special Case of Generalized Gamma Mixture Model

2019 ◽  
Vol 8 (6) ◽  
pp. 1932-1998
Keyword(s):  
Model Selection ◽  
Mixture Model ◽  
Growth Model ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Software Failures ◽  
Generalized Gamma ◽  
Software Reliability Growth

This paper pinpoints to detect and eliminate the actual software failures efficiently. The approach fit in a particular case of Generalized Gamma Mixture Model (GGMM), namely Weibull distribution. The approach estimates two parameters using Maximum Likelihood Estimate (MLE). Standard Evaluation metrics like Mean Square Error (MSE), Coefficient of Determination (R2), Sum of Squares (SSE), and Root Means Square Error (RMSE) were calculated. For the justification of the model selection and goodness of fit various model selection frameworks like Chi-Square Goodness of Fit, Wald’s Test, Akaike Information Criteria (AIC), AICc and Schwarz criterion (SBC) were also estimated. The experimentation was carried out on five benchmark datasets which interpret the considered novel technique identifies the actual failures on par with the existing models. This paper presents a novel software reliability growth model which is more effectual in the identification of the failures significantly and help the present software organizations in the release of software free from bugs just in time

scholarly journals Testing effort based software reliability assessment incorporating FRF and change point

2020 ◽  
Vol 30 (3) ◽  
pp. 273-288
Author(s):  
Rajat Arora ◽  
Anu Aggarwal
Keyword(s):  
Software Reliability ◽  
Change Point ◽  
Goodness Of Fit ◽  
Removal Rate ◽  
Reduction Factor ◽  
Software Systems ◽  
Testing And Debugging ◽  
Testing Effort ◽  
Proposed Model ◽  
Software Reliability Growth

In today's World, to meet the demand of high quality and reliable software systems, it is imperative to perform comprehensive testing and debugging of the software code. The fault detection and removal rate may change over time. The time point after which the rates are changed is termed as the change point. Practically, the failure count may not coincide with the total fault count removed from the system. Their ratio is measured by Fault Reduction Factor (FRF). Here, we propose a Weibull testing effort dependent Software Reliability Growth Model with logistic FRF and change point for assessing the failure phenomenon of a software system. The models have been validated on two real software fault datasets. The parameters are estimated using Least squares and various criteria are employed to check the goodness of fit. The comparison is also facilitated with the existing models in literature to demonstrate that proposed model has better performance.

SOFTWARE RELIABILITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORK BASED FLEXIBLE MODEL INCORPORATING FAULTS OF DIFFERENT COMPLEXITY

2008 ◽  
Vol 15 (02) ◽  
pp. 113-127 ◽  
Author(s):  
P. K. KAPUR ◽  
SUNIL K. KHATRI ◽  
MASHAALLAH BASIRZADEH
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Assessment ◽  
Reliability Growth ◽  
Removal Process ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Fault Removal ◽  
Artificial Neural

With growth in demand for zero defects, predicting reliability of software products is gaining importance. Software Reliability Growth Models (SRGM) are used to estimate the reliability of a software product. We have a large number of SRGM; however none of them works across different environments. Recently, Artificial Neural Networks have been applied in software reliability assessment and software reliability growth prediction. In most of the existing research available in the literature, it is considered that similar testing effort is required on each debugging effort. However, in practice, different amount of testing efforts may be required for detection and removal of different type of faults on basis of their complexity. Consequently, faults are classified into three categories on basis of complexity: simple, hard and complex. In this paper we apply neural network methods to build software reliability growth models (SRGM) considering faults of different complexity. Logistic learning function accounting for the expertise gained by the testing team is used for modeling the proposed model. The proposed model assumes that in the simple faults the growth in removal process is uniform whereas, for hard and complex faults, removal process follows logistic growth curve due to the fact that learning of removal team grows as testing progresses. The proposed model has been validated, evaluated and compared with other NHPP model by applying it on two failure/fault removal data sets cited from real software development projects. The results show that the proposed model with logistic function provides improved goodness-of-fit for software failure/fault removal data.

Statistical Aspects of Lifetime in the Presence of UV Radiation

2003 ◽  
Vol 125 (1) ◽  
pp. 1-3 ◽  
Author(s):  
Michael I. Zeifman ◽  
Dov Ingman
Keyword(s):  
Fiber Optics ◽  
Goodness Of Fit ◽  
High Reliability ◽  
Strong Dependence ◽  
Reliability Function ◽  
Lifetime Distribution ◽  
Quantitative Prediction ◽  
Accurate Estimation ◽  
Ratio Test ◽  
Proposed Model

The reliability function of a component cannot be satisfactorily estimated from experiments, because: (i) an accurate estimation of the lifetime distribution tails, controlling the most important domain of high reliability, requires a very large sample and (ii) reliability tests under normal operational conditions are necessarily very lengthy. Hence the urgent need for a physical model for component lifetime statistics. The paper presents an application of the recently developed model for damage accumulation in polymeric materials to the long-term constant stress rupture experiments on Kevlar Composite, which is widely used in fiber optics. The strong dependence of the experimentally observed distribution shape on the load applied to a component has been previously explained in the framework of two different damage mechanisms: kinetic crack growth and chemical deterioration; the resultant 3-parameter lifetime distribution was predicted to be essentially non-Weibull. The proposed model, based on a single micro-mechanical damage mechanism, leads to a 2-parameter Weibull lifetime distribution with the shape parameter depending on the applied load by a simple inverse power law. Both distribution models were fitted to experimental lifetime data for different stress levels and the corresponding goodness of fit was compared by the usual likelihood ratio test. The proposed model describes the experimental data better — especially in the most important domain of low stress and long (of the order of years) lifetime. The model is physically sound and permits improved design of the accelerated tests and more accurate interpretation of their results, and finally quantitative prediction of the reliability function of the loaded polymeric component.

scholarly journals A Software Reliability Model with a Weibull Fault Detection Rate Function Subject to Operating Environments

2017 ◽  
Author(s):  
Kwang Yoon Song ◽  
In Hong Chang ◽  
Hoang Pham
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Rate Function ◽  
Mean Value ◽  
Software Systems ◽  
Reliability Model ◽  
Function Solution ◽  
Software Reliability Model ◽  
Operating Environments ◽  
Proposed Model

The main focus when developing software is to improve the reliability and stability of a software system. When software systems are introduced, these systems are used in field environments that are the same as or close to those used in the development-testing environment; however, they may also be used in many different locations that may differ from the environment in which they were developed and tested. In this paper, we propose a new software reliability model that takes into account the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness-of-fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models and confidence intervals of all models based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent.

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