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Software project effort estimation with voting rules

Authors:

Johann MitlöhnerAuthors Info & Claims

Decision Support Systems, Volume 46, Issue 4

Pages 895 - 901

https://doi.org/10.1016/j.dss.2008.12.002

Published: 01 March 2009 Publication History

Abstract

Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is again placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present this method and extensions using weightings derived from genetic algorithms. We detail a validation based on several well-known data sets and show that estimation accuracy similar to classic methods can be achieved with considerably lower demands on input data.

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Cited By

Valdés-Souto FValeriano-Assem J(2024)Merging Distinct Sources Databases to Improve Software Estimation ModelsProgramming and Computing Software10.1134/S036176882470076250:8(786-795)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1134/S0361768824700762
Valdés-Souto FTorres-Robledo D(2024)Is It Possible to Use ChatGPT to Perform Measurements Using the COSMIC Method?Programming and Computing Software10.1134/S036176882470069550:8(674-689)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1134/S0361768824700695
Valdés-Souto FNaranjo-Albarrán L(2022)Software Project Estimation Using Smooth Curve Methods and Variable Selection and Regularization Methods as an Alternative to Linear Regression Models when the Reference Database Presents a Wedge-shape FormProgramming and Computing Software10.1134/S036176882208020548:8(716-734)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1134/S0361768822080205
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Software project effort estimation with voting rules

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Published In

cover image Decision Support Systems

Decision Support Systems Volume 46, Issue 4

March, 2009

168 pages

ISSN:0167-9236

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2008.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2009

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Cited By

Valdés-Souto FValeriano-Assem J(2024)Merging Distinct Sources Databases to Improve Software Estimation ModelsProgramming and Computing Software10.1134/S036176882470076250:8(786-795)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1134/S0361768824700762
Valdés-Souto FTorres-Robledo D(2024)Is It Possible to Use ChatGPT to Perform Measurements Using the COSMIC Method?Programming and Computing Software10.1134/S036176882470069550:8(674-689)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1134/S0361768824700695
Valdés-Souto FNaranjo-Albarrán L(2022)Software Project Estimation Using Smooth Curve Methods and Variable Selection and Regularization Methods as an Alternative to Linear Regression Models when the Reference Database Presents a Wedge-shape FormProgramming and Computing Software10.1134/S036176882208020548:8(716-734)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1134/S0361768822080205
Valdés-Souto FNaranjo-Albarrán L(2021)Improving the Software Estimation Models Based on Functional Size through Validation of the Assumptions behind the Linear Regression and the Use of the Confidence Intervals When the Reference Database Presents a Wedge-Shape FormProgramming and Computing Software10.1134/S036176882108025947:8(673-693)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1134/S0361768821080259
(2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.infsof.2017.06.002
Azzeh MNassif AMinku L(2015)An empirical evaluation of ensemble adjustment methods for analogy-based effort estimationJournal of Systems and Software10.1016/j.jss.2015.01.028103:C(36-52)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.jss.2015.01.028
Hu YFeng BMo XZhang XNgai EFan MLiu M(2015)Cost-sensitive and ensemble-based prediction model for outsourced software project risk predictionDecision Support Systems10.1016/j.dss.2015.02.00372:C(11-23)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.dss.2015.02.003
Sarro FVisaggio GCaivano DOivo M(2011)Search-based approaches for software development effort estimationProceedings of the 12th International Conference on Product Focused Software Development and Process Improvement10.1145/2181101.2181111(38-43)Online publication date: 20-Jun-2011
https://dl.acm.org/doi/10.1145/2181101.2181111
Kitchenham BMendes EOstrand T(2009)Why comparative effort prediction studies may be invalidProceedings of the 5th International Conference on Predictor Models in Software Engineering10.1145/1540438.1540444(1-5)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.1145/1540438.1540444

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