A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling

Hsu, Chih Hung; Huang, Chin‐Yu

doi:10.1109/compsac.2010.59

Cited by 16 publications

(19 citation statements)

References 26 publications

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“…Evaluation is made by defining fitness function for each chromosome, this fitness function is an indicator that shows how close this chromosome is to the desired solution [5].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, the meta-heuristics algorithms have gained popularity in solving the optimization problem of scientific fields [5]. Therefore, in this work, we will use the Grey Wolf Optimizer (GWO) to estimate the parameter of the (SRGMs).…”

Section: Introductionmentioning

confidence: 99%

“…The parameters of (SRGMs) are generally unknown and have to be estimated based on collected failure data. Two of the most popular estimation techniques are Maximum Likelihood Estimation (MLE) and Least Squares Estimation (LSE) [5], these two methods are suitable for linear problems but most of the Software Reliability Growth Models are nonlinear, so the researchers are finding many other methods for parameter estimations [6].…”

Section: Introductionmentioning

confidence: 99%

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The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

Alneamy¹,

Dabdoob²

2017

IJCA

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“…Evaluation is made by defining fitness function for each chromosome, this fitness function is an indicator that shows how close this chromosome is to the desired solution [5].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

Alneamy¹,

Dabdoob²

2017

IJCA

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“…Because the formulated model of SRGMs is generally an intrinsically non-linear and multivariable form [3], [5], [20], it is absolutely necessary that we may need to have a faster convergent evolution method for accommodating the weighted assignment of combinational software reliability models [29]. GAs are usually used to provide a possible solution for this problem domain [30]- [32].…”

Section: Related Workmentioning

confidence: 99%

Optimal Weighted Combinational Models for Software Reliability Estimation and Analysis

Hsu

Huang

2014

IEEE Trans. Rel.

Self Cite

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Software is currently a key part of many safety-critical and life-critical application systems. People always need easyand instinctive-to-use software, but the biggest challenge for software engineers is how to develop software with high reliability in a timely manner. To assure quality, and to assess the reliability of software products, many software reliability growth models (SRGMs) have been proposed in the past three decades. The practical problem is that sometimes these selected SRGMs by companies or software practitioners disagree in their reliability predictions, while no single model can be trusted to provide consistently accurate results across various applications. Consequently, some researchers have proposed to use combinational models for improving the prediction capability of software reliability. In this paper, three enhanced weighted-combinations, namely weighted arithmetic, weighted geometric, and weighted harmonic combinations, are proposed. To solve the problem of determining proper weights for model combinations, we further study how to incorporate enhanced genetic algorithms (EGAs) with several efficient operators into weighted assignments. Experiments are performed based on real software failure data, and numerical results show that our proposed models are flexible enough to depict various software development environments. Finally, some management metrics are presented to both assure software quality and determine the optimal release strategy of software products under development.Index Terms-Genetic algorithm, non-homogeneous Poisson process, optimization problems, software development process, software reliability growth models, weighted combinations. ACRONYMS AND ABBREVIATIONSSRGM software reliability growth model SDLC software development life cycle (E)GA (enhanced) genetic algorithm NHPP non-homogeneous Poisson process MVF mean value function GO Goel-Okumoto model Manuscript

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“…In [11][12][13] Particle swarm optimization technique and its variants has been used for parameter estimation and some authors [14][15] also applied Genetic algorithm and its variants for estimation of the parameters for SRGMs. Only three papers have been found [11,14,16] in which evolutionary algorithms have been used for parameter estimation of the SRGM.…”

Section: Related Researchesmentioning

confidence: 99%

Exhausting Meta-heuristic Nature Inspired Approaches for the Parameter Estimation analysis of Software reliability Growth

Sangeeta¹,

Sharma²,

Bala³

2017

Advanced Science and Technology Letters

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Abstract. The parameters are the basic building block for any software reliability growth model. The more accurately the parameters are measured more is the accuracy of the Software Reliability Growth Model (SRGM). So there is an intense requirement of a process with which optimum values of the parameters could be measured. Although there are methods like Maximum Likelihood Estimation (MLE), Least Square Estimation (LSE) etc. but are having various constraints and boundaries while measuring the parameters. The authors in this paper take this challenge of identifying the efficient method with which the parameter values can be optimized. The authors have used time domain datasets for estimation process and conducted experiments on the real world datasets and comparison of the results has been done with the actual failure dataset and among the algorithms used. The authors analyzed the capability of metaheuristic nature inspired algorithms with a new proposed hybrid algorithm for estimating the parameter values of the software reliability growth model. The results obtained are very much satisfactory and might be used on other SRGMs as well and can assess the reliability of any system with more accuracy.

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A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling

Cited by 16 publications

References 26 publications

The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

Optimal Weighted Combinational Models for Software Reliability Estimation and Analysis

Exhausting Meta-heuristic Nature Inspired Approaches for the Parameter Estimation analysis of Software reliability Growth

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