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Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects

Authors:

Marta Fernández-Diego,

José-María Torralba-MartínezAuthors Info & Claims

ESEM '12: Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement

Pages 103 - 106

https://doi.org/10.1145/2372251.2372268

Published: 19 September 2012 Publication History

Abstract

Background: Bayesian networks have been applied in many fields, including effort estimation in software engineering. Even though there are Bayesian inference algorithms than can handle continuous variables, performance tends to be better when these variables are discretized that when they are assumed to follow a specific distribution. On the other hand, the choice of the discretization method and the number of discretized intervals may lead to significantly different estimating results. However, discretization issues are seldom mentioned in software engineering effort estimation models.

Aim: This paper seeks to show that discretization issues are important in terms of prediction accuracy while building a Naive Bayes Classifier (NBC) for estimating software effort.

Method: For this purpose, a NBC model has been developed for software effort estimation based on ISBSG projects applying different discretization schemes (equal width intervals, equal frequency intervals, and k-means clustering) and using different number of intervals.

Results: Regarding the NBC model built, the estimation accuracy of equal frequency discretization is only improved by k-means clustering with respect to Pred(0.25), although it reflects better the original distribution.

Conclusions: Further experimentation should determine the potential of clustering methods already highlighted in other fields.

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Cited By

Van Hai VNhung HProkopova ZSilhavy RSilhavy P(2022)Toward Improving the Efficiency of Software Development Effort Estimation via Clustering AnalysisIEEE Access10.1109/ACCESS.2022.318539310(83249-83264)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3185393
Rai PVerma DKumar S(2021)A hybrid model for prediction of software effort based on team sizeIET Software10.1049/sfw2.1204815:6(365-375)Online publication date: 4-Dec-2021
https://doi.org/10.1049/sfw2.12048
Zare FKhademi Zare HFallahnezhad M(2016)Software effort estimation based on the optimal Bayesian belief networkApplied Soft Computing10.1016/j.asoc.2016.08.00449:C(968-980)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.004
Show More Cited By

Index Terms

Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects
1. Mathematics of computing
  1. Probability and statistics
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation

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Published In

cover image ACM Conferences

ESEM '12: Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement

September 2012

338 pages

ISBN:9781450310567

DOI:10.1145/2372251

General Chair:
Per Runeson
Lund University, Sweden
,
Program Chairs:
Martin Höst
Lund University, Sweden
,
Emilia Mendes
Zayed University, United Arab Emirates
,
Anneliese Andrews
University of Denver, USA
,
Rachel Harrison
Oxford Brookes University, UK

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 September 2012

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ESEM '12

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ESEM '12: 2012 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 19 - 20, 2012

Lund, Sweden

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Cited By

Van Hai VNhung HProkopova ZSilhavy RSilhavy P(2022)Toward Improving the Efficiency of Software Development Effort Estimation via Clustering AnalysisIEEE Access10.1109/ACCESS.2022.318539310(83249-83264)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3185393
Rai PVerma DKumar S(2021)A hybrid model for prediction of software effort based on team sizeIET Software10.1049/sfw2.1204815:6(365-375)Online publication date: 4-Dec-2021
https://doi.org/10.1049/sfw2.12048
Zare FKhademi Zare HFallahnezhad M(2016)Software effort estimation based on the optimal Bayesian belief networkApplied Soft Computing10.1016/j.asoc.2016.08.00449:C(968-980)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.004
Tierno IJ.Nunes D(2013)An Extended Assessment of Data-Driven Bayesian Networks in Software Effort PredictionProceedings of the 2013 27th Brazilian Symposium on Software Engineering10.1109/SBES.2013.17(157-166)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1109/SBES.2013.17

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