Context: An analogy-based software effort estimation technique estimates the required effort for a new software project based on the total effort used in completing past similar projects. In practice, offering high accuracy can be difficult for the technique when the new software project is not similar to any completed projects. In this case, the accuracy will rely heavily on a process called effort adaptation, where the level of difference between the new project and its most similar past projects is quantified and transformed to the difference in the effort. In the past, attempts to adapt to the effort used machine learning algorithms; however, no algorithm was able to offer a significantly higher performance. On the contrary, only a simple heuristic such as scaling the effort by consulting the difference in software size was adopted.

Objective:More recently, million-dollar prize data-science competitions have fostered the rapid development of more powerful machine learning algorithms, such as the Gradient boosting machine and Deep learning algorithm. Therefore, this study revisits the comparison of software effort adaptors that are based on heuristics and machine learning algorithms.

Method:A systematic comparison of software effort estimators, which they all were fully optimized by Bayesian optimization technique, was carried out on 13 standard benchmark datasets. The comparison was supported by robust performance metrics and robust statistical test methods.

Conclusion:The results suggest a novel strategy to construct a more accurate analogy-based estimator by adopting a combined effort adaptor. In particular, the analogy-based model that adapts to the effort by integrating the Gradient boosting machine algorithm and a traditional adaptation technique based on productivity adjustment has performed the best in the study. Particularly, this model significantly outperformed various state-of-the-art effort estimation techniques, including a current standard benchmark algorithmic-based technique, analogy-based techniques, and machine learning-based techniques.

内容：基于类推的软件工作量估算技术会根据用于完成过去类似项目的总工作量来估算新软件项目所需的工作量。在实践中，当新软件项目与任何已完成的项目不相似时，很难为该技术提供高精度。在这种情况下，准确性将在很大程度上取决于称为工作量适应的过程，在该过程中，新项目与其最相似的过去项目之间的差异程度将被量化并转换为工作量差异。过去，尝试使用机器学习算法来适应这种努力。但是，没有算法能够提供明显更高的性能。相反，仅采用一种简单的启发式方法，例如通过咨询软件大小的差异来扩展工作量。

目标：最近，价值一百万美元的数据科学竞赛促进了更强大的机器学习算法的快速发展，例如梯度提升机器和深度学习算法。因此，本研究回顾了基于启发式和机器学习算法的软件努力适配器的比较。

方法：对13个标准基准数据集进行了软件工作量估算器的系统比较，它们全部通过贝叶斯优化技术进行了完全优化。强大的性能指标和强大的统计测试方法为该比较提供了支持。

结论：结果表明，采用联合努力适配器可以构建一种更准确的基于类比的估计量的新颖策略。特别是，通过将Gradient Boosting Machine算法和基于生产率调整的传统适应技术相集成来适应工作量的基于类比的模型在研究中表现最佳。特别是，该模型大大优于各种最新的工作量估算技术，包括当前的基于标准基准算法的技术，基于类比的技术和基于机器学习的技术。