The Software Effort Estimation (SEE) process has been approached in different ways in the literature, including models built from Machine Learning (ML). The combination of these models (Ensemble) is an important research topic in ML, and has lead to improvements in accuracy compared to individual models. This paper proposes heterogeneous and dynamic ensemble selection (DES) models, composed by a set of regressors dynamically selected by classifiers to estimate software development effort. In the training phase, a pool of regression algorithms is trained using training data and a validation data set to validate the models. Next, some classifiers are trained to identify the best regression model from the pool for each training instance. In the test phase each trained classifier is used to dynamically select a regressor model from the pool for predicting the effort for each test instance. The final prediction is given by the combination of the predictions of the regressors selected by the classifiers. An experimental analysis considering a relevant set of software effort estimation problems is reported. The experiments demonstrate that the proposed method outperforms individual regressors and some state of the art models of the literature.

文献中以不同的方式处理了软件工作量估计（SEE）过程，包括从机器学习（ML）构建的模型。这些模型（Ensemble）的组合是ML中的重要研究课题，与单个模型相比，其准确性得到了提高。本文提出了异构和动态集成选择（DES）模型，该模型由分类器动态选择的一组回归变量组成，以估计软件开发工作量。在训练阶段，使用训练数据和验证数据集对一组回归算法进行训练，以验证模型。接下来，对一些分类器进行训练，以从每个训练实例的池中识别出最佳回归模型。在测试阶段，每个训练有素的分类器用于从池中动态选择回归模型，以预测每个测试实例的工作量。最终预测是由分类器选择的回归变量的预测组合给出的。报告了考虑了一组相关的软件工作量估计问题的实验分析。实验表明，所提出的方法优于单个回归器和一些现有模型的文献。