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Heterogeneous Ensemble Dynamic Selection for Software Development Effort Estimation

机译：用于软件开发工作量估算的异构集成动态选择

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摘要

Software development effort estimation is the process of predicting the effort required to develop a software system. In order to improve the estimation accuracy, many different models have been proposed in the literature. Multiple classification systems represent an important field of research for machine learning. In order to estimate software development effort, this paper proposes a heterogeneous and dynamic ensemble selection model, composed by a set of regressors dynamically selected by classifiers. Along with the proposed method it is conducted an experimental analysis involving a relevant set of software effort estimation problems, which has led to better results than those achieved by classical and state of the art models previously presented.
机译：软件开发工作量估算是预测开发软件系统所需工作量的过程。为了提高估计精度，文献中提出了许多不同的模型。多种分类系统代表了机器学习的重要研究领域。为了估计软件开发的工作量，本文提出了一种异构的动态集成选择模型，该模型由分类器动态选择的一组回归器组成。与提出的方法一起，进行了涉及一组相关的软件工作量估计问题的实验分析，与以前提出的经典模型和最新模型相比，该分析导致了更好的结果。

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