Evolutionary neural network classifiers for software effort estimation

Noor Khalaf L. Alhammad, Esra Alzaghoul, Fawaz A. Alzaghoul, Mohammed Akour
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引用次数: 1

Abstract

The estimation of software development efforts has become a crucial activity in software project management. Due to this importance, many researchers focused their efforts on proposing models for relationship construction between efforts and software size and requirements. However, there are still gaps and problems in software effort's estimation process; due to the lack of enough data available in the initial stage of project life cycle. The need for an enhanced and an accurate method for software effort estimation is an urgent issue that challenged software project-management researchers around the world. This work proposes a model based on artificial neural network (ANN) and dragonfly algorithm (DA), in order to provide more accurate model for software effort estimation. The applicability of the model was evaluated using several experiments and the results were in favour of the enhancement with more accurate effort estimation.
用于软件工作量估计的进化神经网络分类器
软件开发工作的评估已经成为软件项目管理中的一项重要活动。由于这种重要性,许多研究人员将他们的努力集中在提出工作与软件规模和需求之间关系构建的模型上。然而,在软件工作的评估过程中仍然存在着差距和问题;由于在项目生命周期的初始阶段缺乏足够的可用数据。迫切需要一种增强的、准确的软件工作量评估方法,这是全世界软件项目管理研究人员面临的一个挑战。本文提出了一种基于人工神经网络(ANN)和蜻蜓算法(DA)的模型,以期为软件工作量估算提供更准确的模型。通过几个实验对模型的适用性进行了评价,结果表明该模型具有更准确的估算力。
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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