A general framework to improve software estimation

改进软件估算的通用框架

• 批准号: 316185-2010
• 负责人: Capretz, Luiz
• 金额: $ 1.68万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Group
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=457589
• 关键词: general; framework; improve; software; estimation

The ability of software engineers to assess projects accurately is a vital issue in the software management discipline. There have been countless attempts in software engineering to identify a specific model, tool, or technique that could be used to solve the challenges of software estimation; unfortunately, most of them have not succeeded. On the other hand, our novel approach to making effort and size estimations shows that a combination of neuro-fuzzy techniques and algorithmic models can greatly improve the accuracy of software project estimation. The results of our work over the past six years have confirmed that, as compared to the use of an algorithmic model alone, the combined neuro-fuzzy framework can produce more accurate software estimations. One paramount feature of our framework is that its architecture is inherently independent of the choice of the algorithmic models or the nature of the prediction problem.

软件工程师准确评估项目的能力在软件管理学科中是一个至关重要的问题。在软件工程中，已经有无数的尝试来确定一个特定的模型、工具或技术，可以用来解决软件评估的挑战；不幸的是，他们中的大多数都没有成功。另一方面，我们对工作量和规模估算的新方法表明，神经模糊技术和算法模型的结合可以极大地提高软件项目估算的准确性。我们过去六年的工作结果已经证实，与单独使用算法模型相比，结合神经模糊框架可以产生更准确的软件估计。我们的框架的一个重要特征是，它的架构本质上独立于算法模型的选择或预测问题的性质。