Analysis and Ranking of Software Reliability Models Based on Weighted Criteria Value

Anjum, Mohd; Haque, Md. Asraful; Ahmad, Nesar

doi:10.5815/ijitcs.2013.02.01

Cited by 48 publications

(22 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many comparison criteria as defined in [22] wherein the authors have presented analysis and ranking of software reliability models based on weighted criteria. The comparison criteria used is measure also called as coefficient of multiple determinations ( which is usually used to depict the goodness-of-fit and is expressible as: [15] ∑ ∑ ∑ ⁄ represents a measure of the percentage of the total variation about the mean for the fitted curve.…”

Section: Results and Comparative Analysismentioning

confidence: 99%

Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal¹

2016

IJMSC

View full text Add to dashboard Cite

A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on NonHomogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.Index Terms: Software Reliability, Software Reliability Growth Model (SRGM), Non-Homogeneous Poisson Process (NHPP), Learning effect, two-type learning effect.

show abstract

Section: Results and Comparative Analysismentioning

confidence: 99%

Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal¹

2016

IJMSC

View full text Add to dashboard Cite

show abstract

“…Geol considered that the time between failures are taken to be exponentially distributed. Different enhancements have been proposed to the existing time between failure models .…”

Section: Related Workmentioning

confidence: 99%

An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

View full text Add to dashboard Cite

Software reliability deals with the probability that software will not cause the failure of a system in a specified time interval. Software reliability growth models (SRGMs) are used to predict future behaviour from known characteristics of software, like historical failures. With the increasing demand to deliver quality software, more accurate SRGMs are required to estimate the software release time and cost of the testing effort. Software failure predictions at early phases also provide an opportunity for investing in proper quality assurance and upfront resource planning. Up till now, many parametric software reliability growth models (PSRGMs) have been proposed. However, several limitations of them mean that their predictive capacities differ from one dataset to others. In this paper, to enhance the prediction accuracy of existing PSRGMs, a high precision error iterative analysis method (HPEIAM) has been proposed based on the residual errors. In HPEIAM, residual errors from the estimated results of SRGMs are considered as another source of data that can combine the residual error modification with artificial neural network sign estimator. The repeated computation of residual errors by SRGMs improves and corrects the prediction accuracy up to the expected level. The performance of HPEIAM is tested with several PSRGMs using two sets of real software failure data based on three performance criteria. Moreover, we have compared the estimated failures predicted by HPEIAM with genetic algorithm (GA)-based prediction improvement. The results demonstrate that HPEIAM gives an improvement in goodness-of-fit and predictive performance for every PSRGM in initial few iterations.

show abstract

“…The seventh criterion is the predicted relative variation (PRV), which is called variance [34][35][36]. It means the standard deviation of the prediction bias and is defined as:…”

Section: Of 17mentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Analysis and Ranking of Software Reliability Models Based on Weighted Criteria Value

Cited by 48 publications

References 22 publications

Analysis of Some Software Reliability Growth Models with Learning Effects

Analysis of Some Software Reliability Growth Models with Learning Effects

An improved software reliability prediction model by using high precision error iterative analysis method

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

Contact Info

Product

Resources

About