Investigating documented information for accurate effort estimation in agile software development

2021 ◽  
Author(s):  
Jirat Pasuksmit
Author(s):  
Emanuel Dantas Filho ◽  
Mirko Perkusich ◽  
Ednaldo Dilorenzo ◽  
Danilo Santos ◽  
Hyggo Almeida ◽  
...  

Author(s):  
Handrie Noprisson

In recent years, the software development methodology evolves from the traditional approach to agile software development. This paper attempted to conduct a systematic literature review (SLR) regarding the improved agile software development to tackle its weakness based on recent research papers. Systematic Reviews and Meta-Analyses (PRISMA) as Systematic Literature Review Method (SLR). SLR is the review method which uses some protocols in order to minimize bias in the reviews. The improved of agile software methodology mostly regarding code reusability, usability, project quality, estimation, software delivery, usability, user responses and requirements delivery, communication between members, usability, practical activities, communication between team and stake holder, usability, workflow (learning), problem identification and effort estimation.


2022 ◽  
pp. 306-328
Author(s):  
Anupama Kaushik ◽  
Devendra Kumar Tayal ◽  
Kalpana Yadav

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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