使用机器学习算法的软件工作量评估

R. Shah, Vrunda Shah, Anuja R. Nair, Tarjni Vyas, Shivani Desai, S. Degadwala
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引用次数: 3

摘要

准确的软件工作评估对于计划、管理和在预算范围内按计划执行一个成功的项目是必不可少的。准确的软件工作评估的必要性是永远不会消失的,因为过高的评估和过低的评估都会给额外软件的开发带来实质性的障碍(SEE)。研究和实践的目的是找到对给定标准和数据集最成功的机器学习估计技术。这是本文研究和实践的目标。大多数从事特定学科的学者都不知道以前的研究结果,这些研究调查了机器学习中不同的工作量估计方法。这项调查的主要目的是帮助软件开发领域的研究人员,帮助他们确定哪种机器学习方法产生最有希望的工作量估计准确性预测。
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Software Effort Estimation using Machine Learning Algorithms
Accurate software work estimates is essential to the planning, management, and execution of a successful project on schedule and within budget. The necessity for accurate software work estimates is something that will never go away since both overestimation and underestimate provide substantial barriers to the development of additional software (SEE). Research and practise are aimed at finding the machine learning estimating technique that is most successful for a given set of criteria and data. This is the goal of the research and practise. Most academics working in a particular subject are not aware of the findings of previous studies that investigated different approaches to effort estimate in machine learning. The primary purpose of this investigation is to aid researchers working in the field of software development by assisting them in determining which method of machine learning produces the most promising effort estimate accuracy prediction.
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