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Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

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  • Arturo Chavoya
  • Cuauhtemoc Lopez-Martin
  • Irma R Andalon-Garcia
  • M E Meda-Campaña

Abstract

Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.

Suggested Citation

  • Arturo Chavoya & Cuauhtemoc Lopez-Martin & Irma R Andalon-Garcia & M E Meda-Campaña, 2012. "Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0050531
    DOI: 10.1371/journal.pone.0050531
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    Cited by:

    1. Mariana Dayanara Alanis-Tamez & Cuauhtémoc López-Martín & Yenny Villuendas-Rey, 2020. "Particle Swarm Optimization for Predicting the Development Effort of Software Projects," Mathematics, MDPI, vol. 8(10), pages 1-21, October.
    2. Yujia Ge & Bin Xu, 2016. "Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-28, June.

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