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中国管理科学

中国管理科学 ›› 2008, Vol. 16 ›› Issue (5): 118-122.

• 论文 • 上一篇    下一篇

基于规模的软件工作量估计方法研究

张俊光1, 吕廷杰2   

  1. 1. 北京科技大学经济管理学院, 北京100083;
    2. 北京邮电大学经济管理学院, 北京100876
  • 收稿日期:2007-04-18 修回日期:2008-09-02 出版日期:2008-10-31 发布日期:2008-10-31
  • 作者简介:张俊光(1972- ),男(汉族),山东省即墨市人,北京科技大学经济管理学院,讲师,博士,研究方向:软件项目管理.
  • 基金资助:

    国家软科学研究计划重大项目(2007GXQ4B117)

Estimation Mathod of Software Project Effort based on Software Size

ZHANG Jun-guang1, LV Ting-Jie2   

  1. 1. School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China;
    2. Beijing University of Posts and Telecommunications, Bijing 100876, China
  • Received:2007-04-18 Revised:2008-09-02 Online:2008-10-31 Published:2008-10-31

PDF (PC)

1784

被引次数

26

摘要： 为获得软件项目的工作量,提出了根据规模进行工作量估计的方法。首先根据组织软件项目的历史数据,通过散点图,确定了工作量和规模之间是高度正相关的线性关系。接下来分别采用公式统计求解法、工具统计求解法进行回归,经过回归后得到了规模和工作量之间的线性回归方程,根据线性回归方程即可获得软件项目的工作量;为简化计算过程,也研究了利用编程统计获得项目工作量的方法。最终研究表明,通过这三种方法所得到的结果基本上是是一致的。通过线性回归方程的实际应用证明,这三种方法是有效的,适用于绝大多数软件组织,其估算误差最高不高于33.57%。

关键词: 软件规模, 工作量估计, 公式统计, 工具统计, 编程统计

Abstract: To obtain the effort of software projects,an effort estimation method based on software size is presented. First,the linear relationship between software effort and size is determined by the scatter diagram generated by the organizations historical data. Next,the formula statistics method and the tool statistics method are adopted independently for linearity regression analysis. After the regression analysis, the linearity regression equation is obtained,according to which the effort could be got.To simplify the calculation process,the programming statistics method is also studied. The final study indicates that the outcomes obtained by these three methods are almost consistent. The application of the linearity regression equation proves that these three methods are effective and they could be applied in most software organizadons. The estimation error is not higher than 33.57%.

Key words: software size, effort estimation, formula statistics, tool statistics, programming statistics

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