基于类比的工作量估计：从数据集特征中发现类比集的新方法
Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics
摘要：
基于类比的工作量估计（ABE）是软件工作量估计的有效方法之一，因为它具有出色的性能和处理噪声数据集的能力。传统的ABE模型通常对数据集中的所有项目使用相同数量的类比，以便进行良好的估计。作者的主张是，使用相同数量的类比可能会为整个数据集产生整体最佳性能，但不一定是每个项目的最佳性能。因此，需要更好地理解数据集的特征，以便为每个项目发现最佳的类比集，而不是使用静态的k个最近的项目。方法：我们提出了一种新的技术的基础上二分法k-medoids聚类算法，提出了最好的一套类比每个单独的项目之前，作出预测。结果与结论：使用二分法，可以更好地理解数据集的特征，并自动为每个测试项目找到最佳的类比集。所提出的估计方法的性能数字是有前途的，优于其他规则ABE模型
英文摘要：
Analogy-based effort estimation (ABE) is one of the efficient methods for software effort estimation because of its outstanding performance and capability of handling noisy datasets. Conventional ABE models usually use the same number of analogies for all projects in the datasets in order to make good estimates. The authors' claim is that using same number of analogies may produce overall best performance for the whole dataset but not necessarily best performance for each individual project. Therefore there is a need to better understand the dataset characteristics in order to discover the optimum set of analogies for each project rather than using a static k nearest projects. Method: We propose a new technique based on Bisecting k-medoids clustering algorithm to come up with the best set of analogies for each individual project before making the prediction. Results & Conclusions: With Bisecting k-medoids it is possible to better understand the dataset characteristic, and automatically find best set of analogies for each test project. Performance figures of the proposed estimation method are promising and better than those of other regular ABE models
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
