arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1703.04565cs.SEcs.AI
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用于早期工作量估计的模糊模型树

Fuzzy Model Tree For Early Effort Estimation

Applied Science University(应用科学大学);Western University(韦仕敦大学)

Mohammad Azzeh, Ali Bou Nassif

摘要:

用例点(UCP)是一种在软件开发早期阶段基于用例图估计项目规模的方法。虽然用例图被广泛接受为一个事实上的模型,用于分析面向对象的软件需求在世界各地,UCP方法没有采取足够的重视,因为,到目前为止,还没有达成共识,如何产生软件工作从UCP。本文的目的是研究使用模糊模型树来获得努力估计的基础上UCP大小测量使用为此目的收集的数据集的潜力。通过Treeboost模型、多元线性回归和基于UCP模型的经典工作量估计对该方法进行了验证。所得结果是有希望的,并显示出更好的性能比经典的UCP,多元线性回归和略优于树boost模型得到的。

英文摘要:

Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Treeboost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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