Journal of Computer Science and Technology

, Volume 22, Issue 3, pp 371–378 | Cite as

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

  • Juan J. Cuadrado Gallego
  • Daniel Rodríguez
  • Miguel Ángel Sicilia
  • Miguel Garre Rubio
  • Angel García Crespo
Regular Paper

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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Supplementary material

11390_2007_9043_MOESM1_ESM.pdf (33 kb)
Supplementary material - Chinese Abstract (PDF 33 kb)

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Copyright information

© Science Press, Beijing, China and Springer Science + Business Media, LLC, USA 2007

Authors and Affiliations

  • Juan J. Cuadrado Gallego
    • 1
  • Daniel Rodríguez
    • 1
  • Miguel Ángel Sicilia
    • 1
  • Miguel Garre Rubio
    • 1
  • Angel García Crespo
    • 2
  1. 1.Department of Computer ScienceThe University of AlcaláAlcaláSpain
  2. 2.Department of Computer ScienceCarlos III UniversityMadridSpain

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