A probabilistic model for predicting software development effort

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We use the naive Bayes model to forecast software effort A causal model is developed from the literature, and a procedure to learn Bayesian prior and conditional probabilities is provided. Using a data set of 40 real-life software projects we test our model. Our results indicate that the probabilistic forecasting models allow managers to estimate joint probability distribution over different software effort estimates. A software project manager may use the joint probability distribution to develop a cumulative probability distribution, which in turn may help the manager estimate the uncertainty that the project effort may be greater than the estimated effort.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsVipin Kumar, Vipin Kumar, Marina L. Gavrilova, Chih Jeng Kenneth Tan, Chih Jeng Kenneth Tan, Pierre L’Ecuyer
PublisherSpringer Verlag
Pages581-588
Number of pages8
ISBN (Print)354040161X
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2668
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Pendharkar, P. C., Subramanian, G. H., & Rodger, J. A. (2003). A probabilistic model for predicting software development effort. In V. Kumar, V. Kumar, M. L. Gavrilova, C. J. K. Tan, C. J. K. Tan, & P. L’Ecuyer (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 581-588). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2668). Springer Verlag. https://doi.org/10.1007/3-540-44843-8_63