.png){kind=icon}
返回
Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.infsof.2013.09.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: Source 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. Objective: We 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. Method: We 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. Results: Based 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). Conclusion: We 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. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Object-oriented
Code size
Estimation
UML
Class diagrams
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
3.8K
被引数:
7.7K
机构
引用论文
.png){kind=icon}
Exploring the relationships between design measures and software quality in object-oriented systems
PREAI
分享收藏分享收藏
查看更多内容