论文标题
关于项目生产力的价值，以提早努力估算
On the Value of Project Productivity for Early Effort Estimation
论文作者
论文摘要
通常，使用用例（UCP）大小估算软件工作需要将生产力用作第二个预测因素。但是，这种方法存在三个缺点：（1）没有明确的程序来预测早期阶段的生产力，（2）使用固定或有限的生产率比不允许研究反映软件行业的现实，并且（3）历史数据中的生产力通常具有挑战性。现在可用的新型UCP数据集允许我们对生产力变量进行进一步的实证研究，以估算UCP的工作。因此，使用了基于生产率的四个不同的预测模型。结果表明，从历史数据中学习生产力比使用依赖默认或有限生产率值的经典方法更有效。此外，从历史环境因素中预测生产力通常不准确。从这里开始，我们得出结论，生产力是在存在和不存在先前历史数据的情况下基于UCP估算软件工作的有效因素。此外，当有历史数据可用时，生产率测量应具有灵活性和可调性
In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting productivity in the early stages, (2) the use of fixed or limited productivity ratios does not allow research to reflect the realities of the software industry, and (3) productivity from historical data is often challenging. The new UCP datasets now available allow us to perform further empirical investigations of the productivity variable in order to estimate the UCP effort. Accordingly, four different prediction models based on productivity were used. The results showed that learning productivity from historical data is more efficient than using classical approaches that rely on default or limited productivity values. In addition, predicting productivity from historical environmental factors is not often accurate. From here we conclude that productivity is an effective factor for estimating the software effort based on the UCP in the presence and absence of previous historical data. Moreover, productivity measurement should be flexible and adjustable when historical data is available
