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Feature subset selection can improve software cost estimation accuracy

Authors:

Zhihao Chen,

Tim Menzies,

Dan Port,

Barry BoehmAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 30, Issue 4

Pages 1 - 6

https://doi.org/10.1145/1082983.1083171

Published: 15 May 2005 Publication History

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Abstract

Cost estimation is important in software development for controlling and planning software risks and schedule. Good estimation models, such as COCOMO, can avoid insufficient resources being allocated to a project. In this study, we find that COCOMO's estimates can be improved via WRAPPER- a feature subset selection method developed by the data mining community. Using data sets from the PROMISE repository, we show WRAPPER significantly and dramatically improves COCOMO's predictive power.

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Din FKhalid SFayaz MGwak JZamli KMashwani W(2022)Fuzzy Adaptive Teaching Learning-Based Optimization for Solving Unconstrained Numerical Optimization ProblemsMathematical Problems in Engineering10.1155/2022/22217622022(1-17)Online publication date: 30-Apr-2022
https://doi.org/10.1155/2022/2221762
Bodendorf FMerkl PFranke J(2021)Intelligent cost estimation by machine learning in supply managementComputers and Industrial Engineering10.1016/j.cie.2021.107601160:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.cie.2021.107601
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Index Terms

Feature subset selection can improve software cost estimation accuracy
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
      2. Software management
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
  2. Software notations and tools
    1. Software configuration management and version control systems

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 30, Issue 4

July 2005

1514 pages

ISSN:0163-5948

DOI:10.1145/1082983

Issue’s Table of Contents

PROMISE '05: Proceedings of the 2005 workshop on Predictor models in software engineering
May 2005
46 pages
ISBN:1595931252
DOI:10.1145/1083165

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 15 May 2005

Published in SIGSOFT Volume 30, Issue 4

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Din FKhalid SFayaz MGwak JZamli KMashwani W(2022)Fuzzy Adaptive Teaching Learning-Based Optimization for Solving Unconstrained Numerical Optimization ProblemsMathematical Problems in Engineering10.1155/2022/22217622022(1-17)Online publication date: 30-Apr-2022
https://doi.org/10.1155/2022/2221762
Bodendorf FMerkl PFranke J(2021)Intelligent cost estimation by machine learning in supply managementComputers and Industrial Engineering10.1016/j.cie.2021.107601160:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.cie.2021.107601
Noever‐Castelos PArdizzone LBalzani C(2021)Model updating of wind turbine blade cross sections with invertible neural networksWind Energy10.1002/we.268725:3(573-599)Online publication date: 21-Oct-2021
https://doi.org/10.1002/we.2687
Wani ZGiri KBashir R(2019)A Generic Data Mining Model for Software Cost Estimation Based on Novel Input Selection ProcedureInternational Journal of Information Retrieval Research10.4018/IJIRR.20190101029:1(16-32)Online publication date: 1-Jan-2019
https://doi.org/10.4018/IJIRR.2019010102
Tran TNguyen VTruong TTran CLe PMinku LKhomh FPetrić J(2019)An Evaluation of Parameter Pruning Approaches for Software EstimationProceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3345629.3345633(26-35)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3345629.3345633
Al Asheeri MHammad M(2019)Machine Learning Models for Software Cost Estimation2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)10.1109/3ICT.2019.8910327(1-6)Online publication date: Sep-2019
https://doi.org/10.1109/3ICT.2019.8910327
(2018)Duplex output software effort estimation model with self-guided interpretationInformation and Software Technology10.1016/j.infsof.2017.09.01094:C(1-13)Online publication date: 1-Feb-2018
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Fernández-Diego MGonzález-Ladrón-De-Guevara F(2018)Application of mutual information-based sequential feature selection to ISBSG mixed dataSoftware Quality Journal10.1007/s11219-017-9391-526:4(1299-1325)Online publication date: 1-Dec-2018
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Gautam SSingh V(2018)The state‐of‐the‐art in software development effort estimationJournal of Software: Evolution and Process10.1002/smr.198330:12Online publication date: 12-Dec-2018
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El Bajta MIdri ARos JFernández-Alemán JToval A(2017)Software Cost Attributes in Global Software Development ProjectsProceedings of the 9th International Conference on Information Management and Engineering10.1145/3149572.3149607(96-101)Online publication date: 9-Oct-2017
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