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Analysis of task effort estimation accuracy based on use case point size

机译：基于用例点大小的工作量估算准确性分析

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摘要

The use case point (UCP) method is one of the most commonly used size estimation methods in software development. Applicability of UCP size for the project effort estimation is thoroughly investigated; however, little attention is devoted to the effort estimation of particular task types. The authors have created and cross-compared prediction models for estimating task-type efforts by means of UCP size using an Online analytical processing model and R packages on a set of 32 real-world projects, with the goal of facilitating analysis of the correlation between project sizes and effort required to complete task types. Requirements, scoping, functional specification, and functional testing task types have up to two times better estimation accuracies than project effort. Implementation has slightly better accuracy than the project effort, while the other task types are not correlated to the UCP size. Using estimates of the most correlated task types and other techniques, such as expert judgment for others, we improved the overall project effort prediction accuracy and decreased the error from 26 to 16%.
机译：用例点（UCP）方法是软件开发中最常用的大小估计方法之一。全面研究了UCP大小在项目工作量估算中的适用性；但是，很少有注意力放在特定任务类型的工作量估计上。作者已经创建并交叉比较了预测模型，以通过使用在线分析处理模型和R包在32个现实项目中使用UCP大小来估计UCP大小的任务类型，目的是促进分析之间的相关性。完成任务类型所需的项目规模和工作量。需求，范围，功能规格和功能测试任务类型的估算精度比项目工作高出两倍。与项目工作相比，实现的准确性略高一些，而其他任务类型与UCP大小无关。通过使用最相关的任务类型和其他技术（例如其他专家的判断）的估计，我们提高了总体项目工作量预测的准确性，并将错误率从26％降低到16％。

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