scholarly journals Software Reliability Growth Model with Partial Differential Equation for Various Debugging Processes

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jiajun Xu ◽  
Shuzhen Yao
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Software Reliability ◽  
Growth Models ◽  
Mean Value ◽  
Statistical Information ◽  
Reliability Growth ◽  
Partial Differential ◽  
Imperfect Debugging ◽  
Software Reliability Growth

Most Software Reliability Growth Models (SRGMs) based on the Nonhomogeneous Poisson Process (NHPP) generally assume perfect or imperfect debugging. However, environmental factors introduce great uncertainty for SRGMs in the development and testing phase. We propose a novel NHPP model based on partial differential equation (PDE), to quantify the uncertainties associated with perfect or imperfect debugging process. We represent the environmental uncertainties collectively as a noise of arbitrary correlation. Under the new stochastic framework, one could compute the full statistical information of the debugging process, for example, its probabilistic density function (PDF). Through a number of comparisons with historical data and existing methods, such as the classic NHPP model, the proposed model exhibits a closer fitting to observation. In addition to conventional focus on the mean value of fault detection, the newly derived full statistical information could further help software developers make decisions on system maintenance and risk assessment.

An Assessment of Incorporating Log-Logistic Testing Effort Into Imperfect Debugging Delayed S-Shaped Software Reliability Growth Model

2021 ◽  
Vol 9 (3) ◽  
pp. 23-41
Author(s):  
Nesar Ahmad ◽  
Aijaz Ahmad ◽  
Sheikh Umar Farooq
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Least Square ◽  
Model Parameters ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Testing Effort ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Effort Function

Software reliability growth models (SRGM) are employed to aid us in predicting and estimating reliability in the software development process. Many SRGM proposed in the past claim to be effective over previous models. While some earlier research had raised concern regarding use of delayed S-shaped SRGM, researchers later indicated that the model performs well when appropriate testing-effort function (TEF) is used. This paper proposes and evaluates an approach to incorporate the log-logistic (LL) testing-effort function into delayed S-shaped SRGMs with imperfect debugging based on non-homogeneous Poisson process (NHPP). The model parameters are estimated by weighted least square estimation (WLSE) and maximum likelihood estimation (MLE) methods. The experimental results obtained after applying the model on real data sets and statistical methods for analysis are presented. The results obtained suggest that performance of the proposed model is better than the other existing models. The authors can conclude that the log-logistic TEF is appropriate for incorporating into delayed S-shaped software reliability growth models.

ON A GENERALIZED DUAL PROCESS SOFTWARE RELIABILITY GROWTH MODEL

1999 ◽  
Vol 06 (01) ◽  
pp. 19-30 ◽  
Author(s):  
PANLOP ZEEPHONGSEKUL ◽  
WINAI BODHISUWAN
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Dual Process ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Numerical Examples ◽  
The Past ◽  
Software Reliability Growth ◽  
Subsequent Introduction

A significant number of software reliability growth models (SRGMs) have been proposed in the literature over the past three decades or so. Most of these models ignored the possibility of imperfect debugging with subsequent introduction of new errors into the software system. In this paper, we present an SRGM which consists of a dual process of debugging followed by introduction of errors in the event of an imperfect debugging. The model also allows for the process to be repeated over several stages. The reliability growth of this model is investigated and is shown to exhibit either an exponential or S-shaped curve. Finally, some reliability measures associated with the SRGM are presented together with illustrative numerical examples.

Change point–based software reliability model under imperfect debugging with revised concept of fault dependency

2016 ◽  
Vol 230 (6) ◽  
pp. 579-597 ◽  
Author(s):  
Subhashis Chatterjee ◽  
Ankur Shukla
Keyword(s):  
Software Reliability ◽  
Change Point ◽  
Growth Models ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Fault Dependency ◽  
Different Characteristics ◽  
Types Of Faults

A detailed study about the characteristics of different types of faults is necessary to enhance the accuracy of software reliability estimation. Over the last three decades, some software reliability growth models have been proposed considering the possibility of existence of two types of faults in a software: (1) independent and (2) dependent faults. In these software reliability growth models, it is considered that the removal of a leading fault or independent fault causes detection of corresponding dependent faults. In practical, it is noticed that some dependent faults are possible in a software which are removed during the removal of other faults. Moreover, dependent faults may have different characteristics, which cannot be ignored. Considering these facts, a detailed study about the different characteristics of both dependent and independent faults has been performed, and based on this study, dependent faults have been categorized into different categories. Furthermore, a new software reliability growth model has been proposed with revised concept of fault dependency under imperfect debugging by introducing the fault removal proportionality. In addition, the effect of change point on model’s parameters due to different environmental factors has been considered. The fault reduction factor is considered as a proportionality function. Experimental results establish the fact that the performance of the proposed model is better with respect to estimated and predicted cumulative number of faults on some real software failure datasets.

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

2021 ◽  
pp. 1748006X2110337
Author(s):  
Vishal Pradhan ◽  
Ajay Kumar ◽  
Joydip Dhar
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Time Lag ◽  
Reduction Factor ◽  
Release Time ◽  
Data Sets ◽  
Time Lags ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Software Reliability Growth

The fault reduction factor (FRF) is a significant parameter for controlling the software reliability growth. It is the ratio of net fault correction to the number of failures encountered. In literature, many factors affect the behaviour of FRF, namely fault dependency, debugging time-lag, human learning behaviour and imperfect debugging. Besides this, several distributions, for example, inflection S-shaped, Weibull and Exponentiated-Weibull, are used as FRF. However, these standard distributions are not flexible to describe the observed behaviour of FRFs. This paper proposes three different software reliability growth models (SRGMs), which incorporate a three-parameter generalized inflection S-shaped (GISS) distribution as FRF. To model realistic SRGMs, time lags between fault detection and fault correction processes are also incorporated. This study proposed two models for the single release, whereas the third model is designed for multi-release software. Moreover, the first model is in perfect debugging, while the rest of the two are in an imperfect debugging environment. The extensive experiments are conducted for the proposed models with six single release and one multi-release data-sets. The choice of GISS distribution as an FRF improves the software reliability evaluation in comparison with the existing systems in the literature. Finally, the development cost and optimal release time are calculated in a perfect debugging environment.

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