Objective: This paper investigates the potential of ensemble learning for
variants of adjustment methods used in analogy-based effort estimation. The
number k of analogies to be used is also investigated. Method We perform a
large scale comparison study where many ensembles constructed from n out of 40
possible valid variants of adjustment methods are applied to eight datasets.
The performance of each method was evaluated based on standardized accuracy and
effect size. Results: The results have been subjected to statistical
significance testing, and show reasonable significant improvements on the
predictive performance where ensemble methods are applied. Conclusion: Our
conclusions suggest that ensembles of adjustment methods can work well and
achieve good performance, even though they are not always superior to single
methods. We also recommend constructing ensembles from only linear adjustment
methods, as they have shown better performance and were frequently ranked
higher.

本研究调查了集成学习在类比式努力估计中的应用潜力，比较了使用的类比数 k，研究发现，将集成方法应用于调节方法可以显著提高预测性能，而线性调节方法的表现更佳，推荐使用线性调节方法进行集成。

基于类比的项目工作量评估中集成校准方法的实证评估

An empirical evaluation of ensemble adjustment methods for analogy-based  effort estimation

The need to update the calibration of Function Point (FP) complexity weights
is discussed, whose aims are to fit specific software application, to reflect
software industry trend, and to improve cost estimation. Neuro-Fuzzy is a
technique that incorporates the learning ability from neural network and the
ability to capture human knowledge from fuzzy logic. The empirical validation
using ISBSG data repository Release 8 shows a 22% improvement in software
effort estimation after calibration using Neuro-Fuzzy technique.

讨论了更新功能点 (FP) 复杂度权重校准的必要性，旨在适用于特定的软件应用、反映软件行业趋势和提高成本估计，并使用神经模糊技术进行实证验证，结果显示使用神经模糊技术进行校准后软件工作量估计改进了 22%。