scholarly journals Convergence rate of Artificial Neural Networks for estimation in software development projects

2021 ◽  
pp. 106627
Author(s):  
Dragica Rankovic ◽  
Nevena Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Development ◽  
Convergence Rate ◽  
Development Projects ◽  
Artificial Neural

scholarly journals Risk Management in Software Development using Artificial Neural Networks

2014 ◽  
Vol 93 (19) ◽  
pp. 22-28
Author(s):  
Amrita Gandhi ◽  
Ajit Naik ◽  
Kapil Thakkar ◽  
Manisha Gahirwal
Keyword(s):  
Neural Networks ◽  
Risk Management ◽  
Artificial Neural Networks ◽  
Software Development ◽  
Artificial Neural

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

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.

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

2020 ◽  
Vol 11 (2) ◽  
pp. 50-71 ◽  
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.

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

2022 ◽  
pp. 947-969
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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