Estimating the parameters of software reliability growth models using hybrid DEO-ANN algorithm

Lohmor, Shailee; Sagar, Bharat Bhushan

doi:10.1504/ijenm.2017.087437

Cited by 5 publications

(4 citation statements)

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“…The research has utilized SVM as a classifier which is not that efficient as compared to neural network classification. [S.Lohmor and B.B.Sagar in 2017] [22] have proposed an effective method for the Software reliability growth model with hybridization of dolphin echolocation optimization artificial neural network via parallel computation. Dolphin echolocation optimization has been utilized for weight optimization with ANN structure for the reduction of computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…The second phase is to make the classification algorithm to understand the design of the desired output. The idea is to classify in order to get the presented diagram size and to compare with KLOC to justify how accurate the proposed architecture is [22]. is a binary class classifier and if the classifier has to be utilized for multiple classes, it works with turn by turn concept.…”

Section: Training Of Cocomo-2 Metrics For the Further Size Estimationmentioning

confidence: 99%

“…11 and Table 4 depicts the comparison of MSE for proposed work and existing work by S. Lohmor and B.B. Sagar (2017) [22]. MSE is the mean of the squares of the difference among the observed value and the estimated value of the software errors being detected.…”

Section: = (7)mentioning

confidence: 99%

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An Advanced Mechanism for Software Size Estimation Using Combinational Artificial Intelligence

Attri¹,

Bal²

2019

IJIES

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Software Size Estimation is the most essential and crucial calculations of the Software Development Life Cycle (SDLC) process. If done wisely, it may accumulate a large amount and time and if done badly, it may cost a lot of amounts. In this modern era of development, traditional methods of estimation do not stand a chance to provide output precisely. Constructive Cost Model (COCOMO)-2 is one of finest calculation methods for size and cost. This paper presents an automated early size estimation technique using Artificial Intelligence (AI). The categorization of the size estimation is into three parts, that is, UML diagrams/code folder mapping via COCOMO-2, COCOMO-2 metrics training, and the classification process to have an appropriate size. This paper focused on regression based training of AI which makes the estimation model more precise. For the evaluation, Mean Square Error (MSE) and Size estimation have been considered according to project samples. It has been seen that the proposed mechanism has attained a minimum MSE of 0.0115 and the difference in sizes is also not high. The comparison has also been done to depict the efficacy of the proposed work with A. B. Nassif et al. and S. Lohmor with B.B. Sagar for True positive rate and Mean square error.

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Section: Related Workmentioning

confidence: 99%

Section: Training Of Cocomo-2 Metrics For the Further Size Estimationmentioning

confidence: 99%

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An Advanced Mechanism for Software Size Estimation Using Combinational Artificial Intelligence

Attri¹,

Bal²

2019

IJIES

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“…Swarm intelligence optimization is a new heuristic computing method based on the observation and inspiration of the behavior of aggregative organisms. Based on these swarm intelligence optimizations, new [4][5][6] and hybrid algorithms [7,8] are proposed to obtain better solutions.…”

Section: Introductionmentioning

confidence: 99%

Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison

Zhang

et al. 2022

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Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC.

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Sustainability-based enterprise supply chain optimization and response under circular economy approach: agile, adaptive and coordinated

Wu,

Wang

2023

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PurposeFrom a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.Design/methodology/approachThis paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.FindingsThe final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.Research limitations/implicationsThere are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.Originality/valueThis manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

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Estimating the parameters of software reliability growth models using hybrid DEO-ANN algorithm

Cited by 5 publications

References 0 publications

An Advanced Mechanism for Software Size Estimation Using Combinational Artificial Intelligence

An Advanced Mechanism for Software Size Estimation Using Combinational Artificial Intelligence

Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison

Sustainability-based enterprise supply chain optimization and response under circular economy approach: agile, adaptive and coordinated

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