| Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering |
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| 作者姓名： | Daniel Rodríguez Angel García Crespo |
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| 作者单位： | [1]Department of Computer Science, The University of Alcalá, Alcalá, Spain [2]Department of Computer Science, Carlos Ⅲ University, Madrid, Spain |
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| 基金项目： | This work is supported by the Spanish Ministry of Science and Technology under Grant No. CICYT TIN2004-06689-C03. |
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| 摘 要： | Parametric software effort estimation models usually consists of only a single mathematical relationship.With the advent of software repositories containing data from heterogeneous projects,these types of models suffer from poor adjustment and predictive accuracy.One possible way to alleviate this problem is the use of a set of mathematical equations obtained through dividing of the historical project datasets according to different parameters into subdatasets called parti- tions.In turn,partitions are divided into clusters that serve as a tool for more accurate models.In this paper,we describe the process,tool and results of such approach through a case study using a publicly available repository,ISBSG.Results suggest the adequacy of the technique as an extension of existing single-expression models without making the estimation process much more complex that uses a single estimation model.A tool to support the process is also presented.
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| 关 键 词： | 软件项目 工作强度估计 多参数模型 数据聚类 |
| 收稿时间： | 15 May 2006 |
| 修稿时间： | 2006-03-152007-02-15 |
Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering |
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| Juan J. Cuadrado Gallego,Miguel ngel Sicilia,Miguel Garre Rubio.Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering[J].Journal of Computer Science and Technology,2007,22(3):371-378. |
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| Authors: | Juan J Cuadrado Gallego Miguel ngel Sicilia Miguel Garre Rubio |
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| Affiliation: | (1) Department of Computer Science, The University of Alcalá, Alcalá, Spain;(2) Department of Computer Science, Carlos III University, Madrid, Spain |
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| Abstract: | Parametric software effort estimation models usually consists of only a single mathematical relationship. With the advent of software repositories containing data from heterogeneous projects, these types of models suffer from poor adjustment and predictive accuracy. One possible way to alleviate this problem is the use of a set of mathematical equations obtained through dividing of the historical project datasets according to different parameters into subdatasets called partitions. In turn, partitions are divided into clusters that serve as a tool for more accurate models. In this paper, we describe the process, tool and results of such approach through a case study using a publicly available repository, ISBSG. Results suggest the adequacy of the technique as an extension of existing single-expression models without making the estimation process much more complex that uses a single estimation model. A tool to support the process is also presented. Keywords software engineering, software measurement, effort estimation, clustering |
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| Keywords: | software engineering software measurement effort estimation clustering |
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