A probabilistic model for predicting software development effort

Parag C. Pendharkar; Girish H. Subramanian; James A. Rodger

doi:10.1007/3-540-44843-8_63

A probabilistic model for predicting software development effort

School of Business Administration (Harrisburg)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Scopus citations

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 language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Vipin Kumar, Vipin Kumar, Marina L. Gavrilova, Chih Jeng Kenneth Tan, Chih Jeng Kenneth Tan, Pierre L’Ecuyer
Publisher	Springer Verlag
Pages	581-588
Number of pages	8
ISBN (Print)	354040161X
DOIs	https://doi.org/10.1007/3-540-44843-8_63
State	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2668
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/3-540-44843-8_63

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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

Pendharkar, Parag C. ; Subramanian, Girish H. ; Rodger, James A. / A probabilistic model for predicting software development effort. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Vipin Kumar ; Vipin Kumar ; Marina L. Gavrilova ; Chih Jeng Kenneth Tan ; Chih Jeng Kenneth Tan ; Pierre L’Ecuyer. Springer Verlag, 2003. pp. 581-588 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Pendharkar, PC , Subramanian, GH & Rodger, JA 2003, A probabilistic model for predicting software development effort. in V Kumar, V Kumar, ML Gavrilova, CJK Tan, CJK Tan & P L’Ecuyer (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2668, Springer Verlag, pp. 581-588. https://doi.org/10.1007/3-540-44843-8_63

A probabilistic model for predicting software development effort. / Pendharkar, Parag C.; Subramanian, Girish H.; Rodger, James A.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Vipin Kumar; Vipin Kumar; Marina L. Gavrilova; Chih Jeng Kenneth Tan; Chih Jeng Kenneth Tan; Pierre L’Ecuyer. Springer Verlag, 2003. p. 581-588 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2668).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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T1 - A probabilistic model for predicting software development effort

AU - Pendharkar, Parag C.

AU - Subramanian, Girish H.

AU - Rodger, James A.

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AB - 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.

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Pendharkar PC , Subramanian GH, Rodger JA. A probabilistic model for predicting software development effort. In Kumar V, Kumar V, Gavrilova ML, Tan CJK, Tan CJK, L’Ecuyer P, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 581-588. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44843-8_63

A probabilistic model for predicting software development effort

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