Segmented software cost estimation models based on fuzzy clustering

  • Authors:

  • Javier Aroba;Juan J. Cuadrado-Gallego;Miguel-Ángel Sicilia;Isabel Ramos;Elena García-Barriocanal

  • Affiliations:

  • Information Technologies Department, University of Huelva, Ctra. Huelva-La Rábida s/n, 21071 Huelva, Spain;Computer Science Department, University of Alcalá, Campus Universitario, Ctra. Barcelona km. 33.6 28871 Alcalá de Henares, Madrid, Spain;Computer Science Department, University of Alcalá, Campus Universitario, Ctra. Barcelona km. 33.6 28871 Alcalá de Henares, Madrid, Spain;Dpto. Lenguajes y Sistemas Informáticos, University of Sevilla, Avda. Reina Mercedes s/n, Sevilla, Spain;Computer Science Department, University of Alcalá, Campus Universitario, Ctra. Barcelona km. 33.6 28871 Alcalá de Henares, Madrid, Spain

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2008

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Abstract

Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees. This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart.