Measuring Function Points following the standard process is sometimes long and expensive. To solve this problem, several early estimation methods have been proposed. Among these, the “NESMA Estimated” method is one of the most widely used; it has also been selected by the International Function Point User Group as the official early function point analysis method, under the name of ‘High-level FPA’ method. A large-scale empirical study has shown that the High-level FPA method – although sufficiently accurate – tends to underestimate the size of software. Underestimating the size of the software to be developed can easily lead to wrong decisions, which can even result in project failure. In this paper we investigate the reasons why the High-level FPA method tends to underestimate. We also explore how to improve the method to make it more accurate. Finally, we propose size estimation models built using different criteria and we evaluate the estimation accuracy of these new models. Our results show that it is possible to derive size estimation models from historical data using simple regression techniques: these models are slightly less accurate than those delivered by the High-level FPA method in terms of absolute estimation errors, but can be used earlier than the High-level FPA method, are cheaper, and do not underestimate software size.

按照标准流程测量功能点有时会很长且很昂贵。为了解决这个问题，已经提出了几种早期估计方法。其中，“ NESMA估计”方法是使用最广泛的方法之一。国际功能点用户组还以“高级FPA”方法的名称将其选择为官方的早期功能点分析方法。大规模的经验研究表明，高级FPA方法虽然足够准确，但往往会低估软件的大小。低估要开发的软件的大小很容易导致错误的决策，甚至可能导致项目失败。在本文中，我们调查了高级FPA方法趋于被低估的原因。我们还将探索如何改进该方法以使其更准确。最后，我们提出了使用不同标准构建的尺寸估算模型，并评估了这些新模型的估算准确性。我们的结果表明，可以使用简单的回归技术从历史数据中得出尺寸估算模型：就绝对估算误差而言，这些模型的准确性比高级FPA方法所提供的精度稍低，但可以早于使用高级FPA方法更便宜，并且不会低估软件大小。