Article

Feature subset selection can improve software cost estimation accuracy

Authors:

Zhihao Chen,

Tim Menzies,

Dan Port,

Barry BoehmAuthors Info & Claims

PROMISE '05: Proceedings of the 2005 workshop on Predictor models in software engineering

Pages 1 - 6

https://doi.org/10.1145/1083165.1083171

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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Priya Varshini AAnitha Kumari K(2024)Software Effort Estimation Using Stacked Ensemble Technique and Hybrid Principal Component Regression and Multivariate Adaptive Regression SplinesWireless Personal Communications: An International Journal10.1007/s11277-024-11010-9134:4(2259-2278)Online publication date: 17-Apr-2024
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Zhu MLi SKuang YHill VHeimberger AZhai LZhai S(2022)Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspectiveFrontiers in Oncology10.3389/fonc.2022.92424512Online publication date: 2-Aug-2022
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Rhmann WPandey BAnsari G(2022)Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithmsInnovations in Systems and Software Engineering10.1007/s11334-020-00377-018:2(309-319)Online publication date: 1-Jun-2022
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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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Information

Published In

PROMISE '05: Proceedings of the 2005 workshop on Predictor models in software engineering

May 2005

46 pages

ISBN:1595931252

DOI:10.1145/1083165

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

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

Publication History

Published: 15 May 2005

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PROMISE '05

PROMISE '05: Predictor models in software engineering

May 15, 2005

Missouri, St. Louis

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Overall Acceptance Rate 98 of 213 submissions, 46%

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Cited By

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Priya Varshini AAnitha Kumari K(2024)Software Effort Estimation Using Stacked Ensemble Technique and Hybrid Principal Component Regression and Multivariate Adaptive Regression SplinesWireless Personal Communications: An International Journal10.1007/s11277-024-11010-9134:4(2259-2278)Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1007/s11277-024-11010-9
Zhu MLi SKuang YHill VHeimberger AZhai LZhai S(2022)Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspectiveFrontiers in Oncology10.3389/fonc.2022.92424512Online publication date: 2-Aug-2022
https://doi.org/10.3389/fonc.2022.924245
Rhmann WPandey BAnsari G(2022)Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithmsInnovations in Systems and Software Engineering10.1007/s11334-020-00377-018:2(309-319)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11334-020-00377-0
Rankovic NRankovic DIvanovic MLazic L(2021)A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal ArraysIEEE Access10.1109/ACCESS.2021.30578079(26926-26936)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3057807
Song LMinku LYao X(2019)Software Effort Interval Prediction via Bayesian Inference and Synthetic Bootstrap ResamplingACM Transactions on Software Engineering and Methodology10.1145/329570028:1(1-46)Online publication date: 9-Jan-2019
https://dl.acm.org/doi/10.1145/3295700
Song LMinku LYao XLeavens GGarcia APăsăreanu C(2018)A novel automated approach for software effort estimation based on data augmentationProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236052(468-479)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3236052
BaniMustafa A(2018)Predicting Software Effort Estimation Using Machine Learning Techniques2018 8th International Conference on Computer Science and Information Technology (CSIT)10.1109/CSIT.2018.8486222(249-256)Online publication date: Jul-2018
https://doi.org/10.1109/CSIT.2018.8486222
Keshta I(2017)Software Cost Estimation Approaches: A SurveyJournal of Software Engineering and Applications10.4236/jsea.2017.101004610:10(824-842)Online publication date: 2017
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Hosni MIdri AAbran AStaron MMeding WPetersen KGencel C(2017)Investigating heterogeneous ensembles with filter feature selection for software effort estimationProceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement10.1145/3143434.3143456(207-220)Online publication date: 25-Oct-2017
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Wu JKeung J(2017)Utilizing cluster quality in hierarchical clustering for analogy-based software effort estimation2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)10.1109/ICSESS.2017.8342851(1-4)Online publication date: Nov-2017
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