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Software effort estimation based on weighted fuzzy grey relational analysis

Authors:

Mohammad Azzeh,

Peter CowlingAuthors Info & Claims

PROMISE '09: Proceedings of the 5th International Conference on Predictor Models in Software Engineering

Article No.: 8, Pages 1 - 10

https://doi.org/10.1145/1540438.1540450

Published: 18 May 2009 Publication History

Abstract

Delivering accurate software effort estimation has been a research challenge for a long time, where none of the existing estimation methods has proven to consistently deliver an accurate estimate. Previous studies have demonstrated that estimation by analogy (EBA) is a viable alternative to other conventional estimation methods in terms of predictive accuracy. EBA offers a way to use a formal method with data from a past project to derive a new estimate. Two important research areas in EBA are addressed in this paper: software projects similarity measurement and attribute weighting. However, the inherent uncertainty of attribute measurement makes similarity measurement between two software projects subject to considerable uncertainty. To tolerate such inherent uncertainty we propose a new similarity measurement method by combining the advantages of Fuzzy Set Theory and Grey Relational Analysis. In addition, since each attribute has different influence on the project retrieval we propose a new approach to deal with this issue based upon the idea of Kendall's coefficient of concordance between the similarity matrix of project attributes and the similarity matrix of known effort values of the dataset. Our results show improved prediction accuracy when multiple project attributes are used with determined weights.

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Cited By

Azzeh MElsheikh YAlqasrawi Y(2024)Software defect density prediction using grey system theory and fuzzy logicSoft Computing10.1007/s00500-024-10324-xOnline publication date: 25-Nov-2024
https://doi.org/10.1007/s00500-024-10324-x
Azzeh MAlqasrawi YElsheikh Y(2023)A soft computing approach for software defect density predictionJournal of Software: Evolution and Process10.1002/smr.255336:4Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1002/smr.2553
Kumar KSrinivas K(2021)Preliminary performance study of a brief review on machine learning techniques for analogy based software effort estimationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03427-y14:3(2141-2165)Online publication date: 23-Sep-2021
https://doi.org/10.1007/s12652-021-03427-y
Show More Cited By

Index Terms

Software effort estimation based on weighted fuzzy grey relational analysis
1. 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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In recent years, grey relational analysis (GRA), a similarity-based method, has been proposed and used in many applications. However, we found that most traditional GRA methods only consider nonweighted similarity for predicting software development ...
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Published In

cover image ACM Other conferences

PROMISE '09: Proceedings of the 5th International Conference on Predictor Models in Software Engineering

May 2009

268 pages

ISBN:9781605586342

DOI:10.1145/1540438

Conference Chair:
Tom Ostrand
AT&T USA

Copyright © 2009 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2009

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Promise '09

Promise '09: 5th International Workshop on Predictor Models in SE

May 18 - 19, 2009

British Columbia, Vancouver, Canada

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Overall Acceptance Rate 98 of 213 submissions, 46%

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Cited By

Azzeh MElsheikh YAlqasrawi Y(2024)Software defect density prediction using grey system theory and fuzzy logicSoft Computing10.1007/s00500-024-10324-xOnline publication date: 25-Nov-2024
https://doi.org/10.1007/s00500-024-10324-x
Azzeh MAlqasrawi YElsheikh Y(2023)A soft computing approach for software defect density predictionJournal of Software: Evolution and Process10.1002/smr.255336:4Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1002/smr.2553
Kumar KSrinivas K(2021)Preliminary performance study of a brief review on machine learning techniques for analogy based software effort estimationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03427-y14:3(2141-2165)Online publication date: 23-Sep-2021
https://doi.org/10.1007/s12652-021-03427-y
Amazal FIdri A(2021)Estimating software development effort using fuzzy clustering‐based analogyJournal of Software: Evolution and Process10.1002/smr.232433:4Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1002/smr.2324
Abnane IIdri AAbran A(2020)Fuzzy case‐based‐reasoning‐based imputation for incomplete data in software engineering repositoriesJournal of Software: Evolution and Process10.1002/smr.226032:9Online publication date: 3-Sep-2020
https://dl.acm.org/doi/10.1002/smr.2260
Amaral WRivero LJunior GViana DAlbuquerque Ade Paula Barros A(2019)Using Machine Learning Technique for Effort Estimation in Software DevelopmentProceedings of the XVIII Brazilian Symposium on Software Quality10.1145/3364641.3364670(240-245)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1145/3364641.3364670
Amazal FIdri AAbran A(2019)Analysis of cluster center initialization of 2FA‐kprototypes analogy‐based software effort estimationJournal of Software: Evolution and Process10.1002/smr.218031:12Online publication date: 12-Dec-2019
https://dl.acm.org/doi/10.1002/smr.2180
Hosni MIdri AShin SShin DLencastre M(2017)Software effort estimation using classical analogy ensembles based on random subspaceProceedings of the Symposium on Applied Computing10.1145/3019612.3019784(1251-1258)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019784
Idri AHosni MAbran A(2016)Improved estimation of software development effort using Classical and Fuzzy Analogy ensemblesApplied Soft Computing10.1016/j.asoc.2016.08.01249:C(990-1019)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.012
Idri AAmazal FAbran A(2016)Accuracy Comparison of Analogy-Based Software Development Effort Estimation TechniquesInternational Journal of Intelligent Systems10.1002/int.2174831:2(128-152)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1002/int.21748
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