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An Empirical Analysis on Software Development Efforts Estimation in Machine Learning Perspective

机译：机器学习视角下软件开发工作估算的实证分析

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摘要

The prediction of effort estimation is a vital factor in the success of any software development project. The availability of expert systems for the software effort estimation supports in minimization of effort and cost for every software project at the same time leads to timely completion and proper resource management of the project. This article supports software project managers and decision-makers by providing a state-of-the-art empirical analysis of effort estimation methods based on machine learning approaches. In this paper five machine learning techniques; polynomial linear regression, ridge regression, decision trees, support vector regression, and Multilayer Perceptron (MLP) are investigated for software development effort estimation by using benchmark publicly available data sets. The empirical performance of machine learning methods for software effort estimation is investigated on seven standard data sets i.e. Albretch, Desharnais, COCOMO81, NASA, Kemerer, China, and Kitchenham. Furthermore, the performance of software effort estimation approaches is evaluated statistically applying the performance metrics i.e. MMRE, PRED (25), R2-score, MMRE, Pred(25). The empirical results reveal that the decision tree-based techniques on Deshnaris, COCOMO, China, and kitchenham data sets produce more adequate results in terms of all three-performance metrics. On the Albgreenretch and NASA datasets, the ridge regression method outperformed then other techniques except the pred(25) metric where decision trees performed better.
机译：努力估算的预测是任何软件开发项目成功的重要因素。软件努力估算专家系统的可用性在最大限度地支持每个软件项目的最小化和成本中，同时导致项目的及时完成和适当的资源管理。本文通过提供基于机器学习方法的努力估算方法提供最先进的实证分析，支持软件项目经理和决策者。在本文中，五种机器学习技术;通过使用基准公开可用的数据集来研究多项式线性回归，脊回归，决策树，支持向量回归和多层的Perceptron（MLP），用于软件开发工作估算。在七个标准数据集中研究了软件努力估算的机器学习方法的经验性能。旧标准数据集I. albretch，Desharnais，Cocomo81，美国宇航局，克麦甲，中国和厨房。此外，在统计上应用软件工作估计方法的性能，统计上应用性能度量，PRED（25），R2分数，MMRE，PRED（25）。实证结果表明，基于决策树的基于树木，CoCoMo，中国和厨房数据集的技术在所有三种绩效指标方面产生了更充分的结果。在AlbGreenRetch和NASA数据集上，脊回归方法优于其他技术，除了PRED（25）度量，其中决策树更好。

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