An empirical analysis of data preprocessing for machine learning-based software cost estimation
An empirical analysis of data preprocessing for machine learning-based software cost estimation
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DOI：
10.1016/j.infsof.2015.07.004
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发表时间：
2015-11-01
影响因子：
3.9
通讯作者：
Xie, Min
中科院分区：
文献类型：
--
作者：
Huang, Jianglin;Li, Yan-Fu;Xie, Min
内容：由于软件开发过程的复杂性，传统的参数模型和统计方法往往显得不足以模拟项目开发成本和项目特征（或成本驱动因素）之间日益复杂的关系。近年来，机器学习（ML）方法在软件成本估计中得到了广泛的应用。数据预处理被许多研究者认为是机器学习方法的一个基本阶段，然而，很少有研究关注数据预处理技术对机器学习方法的影响。目的：本研究旨在实证评估数据预处理技术在软件成本估算中对机器学习方法的有效性。方法：在这项工作中，我们首先使用数据预处理技术对最近的出版物进行文献调查，然后进行了系统的实证研究，分析了各种数据预处理技术的优缺点，结果：我们的研究结果表明，数据预处理技术可能会显着影响最终的预测。结论：为了减少预测误差，提高效率，需要根据机器学习方法的特点以及软件成本估算所使用的数据集进行仔细的选择。(C)2015爱思唯尔B.V.保留所有权利。
Context: Due to the complex nature of software development process, traditional parametric models and statistical methods often appear to be inadequate to model the increasingly complicated relationship between project development cost and the project features (or cost drivers). Machine learning (ML) methods, with several reported successful applications, have gained popularity for software cost estimation in recent years. Data preprocessing has been claimed by many researchers as a fundamental stage of ML methods; however, very few works have been focused on the effects of data preprocessing techniques.Objective: This study aims for an empirical assessment of the effectiveness of data preprocessing techniques on ML methods in the context of software cost estimation.Method: In this work, we first conduct a literature survey of the recent publications using data preprocessing techniques, followed by a systematic empirical study to analyze the strengths and weaknesses of individual data preprocessing techniques as well as their combinations.Results: Our results indicate that data preprocessing techniques may significantly influence the final prediction. They sometimes might have negative impacts on prediction performance of ML methods.Conclusion: In order to reduce prediction errors and improve efficiency, a careful selection is necessary according to the characteristics of machine learning methods, as well as the datasets used for software cost estimation. (C) 2015 Elsevier B.V. All rights reserved.