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

Anupama Kaushik, D. Tayal, Kalpana Yadav
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引用次数: 11

Abstract

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.
神经网络和元启发式在敏捷软件开发工作量评估中的作用
在任何软件开发中,对资源的准确估计是导致项目开发成功的关键任务之一。在传统的软件开发中,在工作量评估方面已经做了大量的工作。但是,对于敏捷软件开发的工作量评估工作是非常少的。本文提出了一种基于人工神经网络和元启发式技术的敏捷软件开发工作量估算技术。所使用的人工神经网络有径向基函数神经网络(RBFN)和功能链接人工神经网络(FLANN)。使用的元启发式技术是鲸鱼优化算法(WOA),这是一种受自然启发的元启发式技术。在三个敏捷数据集上对所提出的FLANN-WOA和RBFN-WOA技术进行了评估,发现这些神经网络模型在使用元启发式技术时表现非常好。这是进一步的经验验证使用非参数统计检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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