A Review of Software Cost Estimation in Agile Software Development Using Soft Computing Techniques

Saurabh Bilgaiyan, Samaresh Mishra, M. Das
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引用次数: 27

Abstract

For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of development models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different development models having new and innovative phases of software development, is a crucial task to be done. An accurate prediction always leads to a successful software project within the budget with no delay, but any percentage of misconduct in the overall effort and cost estimate may lead to a project failure in terms of delivery time, budget or features. Software industries have adopted various development models based on the project requirements and organization's capabilities. Due to adaptability to changes in a software project, agile software development model has become a much successful and popular framework for development over the last decade. The customer is involved as an active participant in the development using an agile framework. Hence, changes can occur at any phase of development and they can be dynamic in nature. That is why an accurate prediction of effort and cost of such projects is a crucial task to be done as the complexity of overall development structure is increased with the time. Soft computing techniques have proven that they are one of the best problem solving techniques in such scenarios. Such techniques are more flexible and presence of bio-intelligence increases their accuracy. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Inference Systems (FIS), etc. are applied successfully for estimation of cost and effort of agile based software projects. This paper deals with such soft computing techniques and provides a detailed and analytical overview of such methods. It also provides the future scope and possibilities to explore such techniques on the basis of survey provided by this paper.
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基于软计算技术的敏捷软件开发中软件成本估算综述
对于一个成功的软件项目来说,准确地预测其总体工作和成本估算是一项非常重要的任务。在过去的几十年里,软件项目已经发展了许多开发模型。因此,对不同软件项目的工作和成本进行精确的度量是一项至关重要的任务,该项目基于具有软件开发新阶段和创新阶段的不同开发模型。准确的预测总是在预算范围内导致软件项目的成功,没有延迟,但是在总体工作和成本估算中任何比例的不当行为都可能导致项目在交付时间、预算或特性方面的失败。软件行业已经采用了基于项目需求和组织能力的各种开发模型。由于对软件项目变更的适应性,敏捷软件开发模型在过去十年中已经成为一种非常成功和流行的开发框架。客户作为积极的参与者参与到使用敏捷框架的开发中。因此,变化可能发生在开发的任何阶段,它们本质上是动态的。这就是为什么准确预测这些项目的工作量和成本是一项至关重要的任务,因为整个开发结构的复杂性随着时间的推移而增加。软计算技术已经证明,它们是此类场景中最好的问题解决技术之一。这种技术更加灵活,生物智能的存在提高了它们的准确性。遗传算法(GA)、粒子群优化(PSO)、人工神经网络(ANN)、模糊推理系统(FIS)等成功地应用于基于敏捷的软件项目的成本和工作量估算。本文讨论了这些软计算技术,并对这些方法进行了详细的分析概述。在本文调查的基础上,提出了未来探索此类技术的范围和可能性。
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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