Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering  

作　　者：Juan J.Cuadrado Gallego Daniel Rodríguez Miguel Angel Sicilia Miguel Garre Rubio Angel García Crespo 

机构地区：[1]Department of Computer Science The University of Alcalá,Alcalá,Spain [2]Department of Computer Science Carlos Ⅲ University.Madrid,Spain

出　　处：《Journal of Computer Science & Technology》2007年第3期371-378,共8页计算机科学技术学报（英文版）

基　　金：This work is supported by the Spanish Ministry of Science and Technology under Grant No.CICYT TIN2004-06689-C03.

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 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, clusteringParametric 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

关 键 词：software engineering software measurement effort estimation CLUSTERING 

分 类 号：TP311.52[自动化与计算机技术—计算机软件与理论]