A general framework to improve software estimation

软件工程师准确评估项目的能力是软件管理学科中的一个重要问题。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，我们的新方法，使努力和规模的估计表明，神经模糊技术和算法模型的组合，可以大大提高软件项目估计的准确性。我们在过去六年的工作结果已经证实，相比于单独使用算法模型，组合的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。

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.