Effort estimation modeling techniques: a case study for web applications

  • Authors:

  • Gennaro Costagliola;Sergio Di Martino;Filomena Ferrucci;Carmine Gravino;Genoveffa Tortora;Giuliana Vitiello

  • Affiliations:

  • University of Salerno, Fisciano (SA), Italy;University of Salerno, Fisciano (SA), Italy;University of Salerno, Fisciano (SA), Italy;University of Salerno, Fisciano (SA), Italy;University of Salerno, Fisciano (SA), Italy;University of Salerno, Fisciano (SA), Italy

  • Venue:
  • ICWE '06 Proceedings of the 6th international conference on Web engineering
  • Year:
  • 2006

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Abstract

A reliable effort estimation is crucial for a successful web application development planning. Several approaches exist to address this issue. Among them, the algorithmic approach is one of the most widely used and investigated methods. It is based on suitable effort prediction models which relate the development effort with project characteristics. The size represents one of the most interesting characteristics of software products and several measures can be defined in order to estimate the size of web systems. Moreover, several techniques have been proposed in the literature to build the effort prediction models. Thus, of special interest should be to establish the most effective size measures to be employed in effort prediction models and the most suitable techniques for the model construction. To this aim some empirical studies have been undertaken so far. Since it is widely recognized that several investigations should be performed to verify/confirm empirical results, in the paper we will report on an empirical analysis we have carried out by exploiting data coming from 15 web projects developed by a software company. In particular, for the analysis we have considered two sets of size measures: Length Measures (e.g. number of pages, number of medias, number of client and server side scripts) and Functional Measures (e.g. external input, external output, external query). Moreover, we have employed different techniques, such as Linear Regression, Regression Tree, and Analogy-Based Estimation, in order to determine the one that provides the best prediction.