Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study
Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study
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DOI：
10.1016/j.infsof.2013.09.003
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发表时间：
2014-02
期刊：
影响因子：
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通讯作者：
Yuming Zhou;Yibiao Yang;Baowen Xu;Hareton K. N. Leung;Xiaoyu Zhou
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文献类型：
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作者：
Yuming Zhou;Yibiao Yang;Baowen Xu;Hareton K. N. Leung;Xiaoyu Zhou
背景源代码的大小（源代码行）是许多参数化软件工作量估计模型的输入。然而，它是不可用的，在早期阶段的软件development. MethodWe调查的准确性，早期的Score估计方法的面向对象的系统，使用收集的信息，从它的UML类图可在早期的软件developmentphase.MethodWe使用不同的建模技术，建立预测模型，调查六种类型的度量估计Score的准确性。使用的技术包括线性模型、非线性模型、基于规则/树的模型和基于实例的模型。所研究的度量指标有类图度量、预测对象点、面向对象项目规模度量、快速&严肃类点、目标类点和面向对象功能点。我们发现，使用面向对象的项目规模度量和对数变换的普通最小二乘回归建立的预测模型达到了最高的精度（平均MMRE = 0.19，平均Pred（25）= 0.74）。结论应采用面向对象的项目规模度量方法和对数变换的普通最小二乘回归建立简单、准确、易于理解的SSCP估算模型。
BackgroundSource code size in terms of SLOC (source lines of code) is the input of many parametric software effort estimation models. However, it is unavailable at the early phase of software development.ObjectiveWe investigate the accuracy of early SLOC estimation approaches for an object-oriented system using the information collected from its UML class diagram available at the early software development phase.MethodWe use different modeling techniques to build the prediction models for investigating the accuracy of six types of metrics to estimate SLOC. The used techniques include linear models, non-linear models, rule/tree-based models, and instance-based models. The investigated metrics are class diagram metrics, predictive object points, object-oriented project size metric, fast&&serious class points, objective class points, and object-oriented function points.ResultsBased on 100 open-source Java systems, we find that the prediction model built using object-oriented project size metric and ordinary least square regression with a logarithmic transformation achieves the highest accuracy (mean MMRE = 0.19 and mean Pred(25) = 0.74).ConclusionWe should use object-oriented project size metric and ordinary least square regression with a logarithmic transformation to build a simple, accurate, and comprehensible SLOC estimation model.