Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Sheta, Alaa; Abdel-Raouf, Amal

doi:10.14569/ijacsa.2016.070465

Cited by 8 publications

(4 citation statements)

References 45 publications

(38 reference statements)

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“…Following α is β, responsible for supporting α in decision-making and potentially becoming the leader when α is no longer able 45 . Lastly, δ, positioned at the lower hierarchy, typically follows the lead of other wolves in the pack 49 . In a mathematical model, every individual grey wolf signifies a candidate within the population.…”

Section: Grey Wolf Optimizationmentioning

confidence: 99%

A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique

Mallik,

Pradhan,

Muduli

et al. 2024

Preprint

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In software development and testing, detecting and mitigating faults are paramount to prevent potential issues from escalating and disrupting the development and testing processes. The proposed method can also improve the prediction of various is sues, such as increased model complexity, longer execution times, higher error rates, and enhanced fault detection capabilities. Addressing this concern, the paper introduced a three-stage model encompassing data pre-processing, feature dimensionality reduction, and fault prediction, which are essential steps in effective software testing. Our research leverages the publicly available NASA dataset and employs Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to re duce feature vector dimensions, a common practice in software testing. We propose an improved version of the Grey Wolf Optimization (IMGWO) algorithm, complemented by Extreme Learning Machines (ELM), to discern the presence of defects within software modules. This approach is highly relevant in software testing, as it aids in identifying problematic areas early in the development cycle. Utilizing the PCA-LDA+IMGWO-ELM approach, our model achieves an average accuracy rate of 0.9811 when applied to the KC2 dataset, a significant milestone in software testing. These results are substantiated through experimental validation, reinforcing the credibility of our approach in predicting potential software defects during the software testing phase.

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Section: Grey Wolf Optimizationmentioning

confidence: 99%

A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique

Mallik,

Pradhan,

Muduli

et al. 2024

Preprint

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show abstract

“…GWO simulates the social hierarchy and hunting behavior of the grey wolf population [41,42]. The grey wolf population in nature is divided into four grades: α, β, δ, and ω, in order of social status from high to low.…”

Section: Grey Wolf Optimizationmentioning

confidence: 99%

“…Grey wolf optimization (GWO) algorithm [40,41] is a novel algorithm for guiding the group to search the optimal value, which is inspired by wolves' hunting behavior and social hierarchy. It has obvious advantages in global search and convergence [42]. Jin et al [43] proposed a hybrid optimization method using differential evolution and grey wolf optimization.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

2018

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Abstract:In recent years, the construction of China's power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China's power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.

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“…The nature-inspired approach is one of the approaches to overcome these limitations for parameter estimation of NHPP-based software reliability models [21][22][23][24]. However, most efforts have been made to model the debugging phenomena of close-source software.…”

Section: Introductionmentioning

confidence: 99%

Modelling Open-Source Software Reliability Incorporating Swarm Intelligence-Based Techniques

Shatnawi

2023

IJCSIT

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In the software industry, two software engineering development best practices coexist: open-source and closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a proprietary code that only the owner can access. Software reliability is crucial in the industry when a new product or update is released. Applying meta-heuristic optimization algorithms for closed-source software reliability prediction has produced significant and accurate results. Now, open-source software dominates the landscape of cloud-based systems. Therefore, providing results on open- source software reliability - as a quality indicator - would greatly help solve the open-source software reliability growth- modelling problem. The reliability is predicted by estimating the parameters of the software reliability models. As software reliability models are inherently nonlinear, traditional approaches make estimating the appropriate parameters difficult and ineffective. Consequently, software reliability models necessitate a high- quality parameter estimation technique. These objectives dictate the exploration of potential applications of meta- heuristic swarm intelligence optimization algorithms for optimizing the parameter estimation of nonhomogeneous Poisson process-based open-source software reliability modelling. The optimization algorithms are firefly, social spider, artificial bee colony, grey wolf, particle swarm, moth flame, and whale. The applicability and performance evaluation of the optimization modelling approach is demonstrated through two real open-source software reliability datasets. The results are promising

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Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Cited by 8 publications

References 45 publications

A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique

A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

Modelling Open-Source Software Reliability Incorporating Swarm Intelligence-Based Techniques

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