Risk Management in Software Development using Artificial Neural Networks

This study presents a bibliometric analysis of Artificial Neural Networks in Risk Management. The study considered articles from the I.S.I. Web of Knowledge and Scopus databases, Identification of publishers, countries, periodicals and the keywords most frequently cited. We used the CiteSpace® software to analyze this material, which provides a set of features to support bibliometrics, including the reference maps. This study provides data collection on Artificial Neural Networks applied to risk management. The number of works identified in this study is significant, and in the last ten years, the number of citations has increased. We did not identify the increase in paper count within the same period.

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The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

10.4018/978-1-6684-2408-7.ch014 ◽

2022 ◽

pp. 306-328

Author(s):

Anupama Kaushik ◽

Devendra Kumar Tayal ◽

Kalpana Yadav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Software Development ◽

Statistical Tests ◽

Agile Software Development ◽

Effort Estimation ◽

Software Development Effort ◽

Artificial Neural ◽

Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

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The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

International Journal of Information Technology Project Management ◽

10.4018/ijitpm.2020040104 ◽

2020 ◽

Vol 11 (2) ◽

pp. 50-71 ◽

Cited By ~ 2

Author(s):

Anupama Kaushik ◽

Devendra Kumar Tayal ◽

Kalpana Yadav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Software Development ◽

Agile Software Development ◽

Accurate Estimation ◽

Effort Estimation ◽

Whale Optimization ◽

Artificial Neural ◽

Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

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