Estimating parameters of software reliability models by ant colony algorithm

Zheng, Changyou; Liu, Xiaoming; Uang, Song

doi:10.3724/sp.j.1087.2012.01147

Cited by 7 publications

(8 citation statements)

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“…However, neural networks-based models were not easily interpreted; so, some other techniques were needed to be explored. Another technique inspired by GAs and named genetic programming was proposed to obtain software reliability model (SRM) for forecasting the reliability by Oliveira et al 21 Zhang et al 22 applied a PSO algorithm, for the estimation, but it was observed that the searching range is too large, the convergence speed is slow and accuracy is not high. The use of fuzzy logic to build an SRGM has also been explored by Aljahdali and Sheta, 23 who proposed a fuzzy model, which consists of a collection of linear sub-models joined together smoothly using fuzzy membership functions to represent the fuzzy model.…”

Section: Related Workmentioning

confidence: 99%

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A modified whale optimization algorithm for parameter estimation of software reliability growth models

2021

Journal of Algorithms & Computational Technology

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Software reliability growth models are nonlinear in nature, so it is difficult to estimate the proper parameters. An estimation method based on a modified whale optimization algorithm in which parameters are estimated is discussed in this paper. The whale optimization algorithm is a new swarm intelligence optimization algorithm. This algorithm is not perfect enough. Based on the analysis of whale optimization algorithm, we point out the disadvantages of whale optimization algorithm, and propose a modified whale optimization algorithm algorithm from four aspects: choice regarding the dimension, exploration control, encircling prey modified, and candidate solution selection. The experimental results based on 34 benchmark functions demonstrate that the proposed modified whale optimization algorithm has better accuracy. The modified whale optimization algorithm is used to predict software reliability by predicting the faults during the software testing process using software faults’ historical data. The proposed modified whale optimization algorithm shows significant advantages in handling a variety of modeling problems such as the exponential model, power model, delayed s-shaped model, and modified sigmoid model. Experimental results show that the fitting accuracy of the modified sigmoid model model is minimal on three data sets. The modified whale optimization algorithm with the modified sigmoid model can provide a better estimate of the software faults.

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Section: Related Workmentioning

confidence: 99%

“…. 27 The number of measurements collected during the testing process is small. This represents a difficulty for traditional parameter estimation techniques.…”

Section: Test/debug Data Setsmentioning

confidence: 99%

A modified whale optimization algorithm for parameter estimation of software reliability growth models

2021

Journal of Algorithms & Computational Technology

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“…Minohara et al [7] applied GA for parameter estimation of SRGMs and reported better estimation in comparison to LSE and MLE. Zhang et al [8] applied PSO on parameter estimation of SRGMs and reported that it takes large search space and converge slowly. Hsu et al [6] applied modified‐ GA for parameter estimation of SRGMs and reported efficient estimation than GA. CS is applied for parameter estimation of SRGMs by AL‐Saati el al .…”

Section: Related Workmentioning

confidence: 99%

“…Nature inspired approaches overcome these limitations for parameter estimation of SRGMs. Genetic algorithm (GA), real valued GA, ant colony optimisation (ACO), cuckoo search (CS) optimisation, particle swarm optimisation (PSO) and gravitational search algorithm (GSA) are applied for parameter estimation of SRGMs [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Efficient parameter estimation of software reliability growth models using harmony search

2017

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The primary challenge of software reliability growth model is to find the unknown model parameters that are used to validate on software failure dataset. Though, numerical estimation technique plays a vital role in parameter estimation of software reliability growth models, they are not optimal as they suffer from constraints sucha as sample size, biasing, and initialisation of parameters. In this study, a parameter estimation of software reliability growth model that utilises a variant of harmony search is proposed. Extensive experiments are conducted on seven different software datasets of varying complexity. A robust experimental setup is developed employing an orthogonal array and Taguchi method. Twofold performance comparisons are performed. First, the authors tested their proposed approach against Cuckoo search and numerical method (least square estimation) considering mean square error and Theil's statistics as a quality measure. Second, the authors applied statistical tests are performed that demonstrate the superiority of their approach over the others. The underlying motivation to conduct this study is to motivate researchers to utilise their approach for a better estimation of model parameters.

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“…Weibull Curve was used to connect the research efforts at that time. NHPP [8] was the template technique. There have also been suggestions for many other parametric models.…”

Section: Introductionmentioning

confidence: 99%

Statistical Quartile Deviation-Based Software Reliability Growth Estimation Measure for Reliability Prediction

2020

jcr

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Software reliability growth models are used to predict the quality of the software systems using statistical learning models. However,a large number of faults areremained undetected in small and medium applications. A large number of traditional reliability measures are used to test the software faults in theapplication development and testing process. But in real-time, new faults are included in the software testing and maintenance phases in order to find the reliability estimation. The main problem in the existing SRGMs include, difficult to handle large reliability data and these models are not applicable to statistical dependencies and independency measures. In the proposed model, a novel statistical dependency and independency-based quartile density distribution model is implemented to improve the reliability prediction rate. Experimental results proved that the present model has high reliability estimation probability than the traditional growth models in terms of skewness and peak-ness.

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Estimating parameters of software reliability models by ant colony algorithm

Cited by 7 publications

References 0 publications

A modified whale optimization algorithm for parameter estimation of software reliability growth models

A modified whale optimization algorithm for parameter estimation of software reliability growth models

Efficient parameter estimation of software reliability growth models using harmony search

Statistical Quartile Deviation-Based Software Reliability Growth Estimation Measure for Reliability Prediction

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