research-article

Predicting software maintenance using fuzzy model

Authors:

Pradeep Kumar Bhatia,

Omprakash SangwanAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 34, Issue 4

Pages 1 - 6

https://doi.org/10.1145/1543405.1543425

Published: 06 July 2009 Publication History

Abstract

Software maintenance is commonly used to refer to the modifications that are made to a software system after its initial release, installed and is operational. There is evidence that maintenance costs exceed 60 percent of the total costs of software. In this paper we have analyzed the major factors that can affect software maintenance and divide them into four categories: Readability of Source Code (RSC), Documentation Quality (DQ), Understandability of Software (UOS), and Average Cyclomatic Complexity (ACC). In our study we have proposed fuzzy model to predict software maintenance using these four factors. The proposed fuzzy model is validated and experimental results indicate that the proposed model is suitable for predicting software maintenance level of the software.

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Ilyas KJabeen TMustafa G(2021)A Survey on the Use of Computational Intelligence Techniques in Software Engineering2021 International Conference on Innovative Computing (ICIC)10.1109/ICIC53490.2021.9709625(1-8)Online publication date: 9-Nov-2021
https://doi.org/10.1109/ICIC53490.2021.9709625
Sharma KDubey SGaurav PPrachi (2020)Functionality Assessment of Software System using Fuzzy Approach2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO48877.2020.9197795(1206-1209)Online publication date: Jun-2020
https://doi.org/10.1109/ICRITO48877.2020.9197795
Saini GPanwar DKumar SSingh V(2020)A systematic literature review and comparative study of different software quality modelsJournal of Discrete Mathematical Sciences and Cryptography10.1080/09720529.2020.174718823:2(585-593)Online publication date: 14-May-2020
https://doi.org/10.1080/09720529.2020.1747188
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cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 34, Issue 4

July 2009

152 pages

ISSN:0163-5948

DOI:10.1145/1543405

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Copyright © 2009 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2009

Published in SIGSOFT Volume 34, Issue 4

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Cited By

Ilyas KJabeen TMustafa G(2021)A Survey on the Use of Computational Intelligence Techniques in Software Engineering2021 International Conference on Innovative Computing (ICIC)10.1109/ICIC53490.2021.9709625(1-8)Online publication date: 9-Nov-2021
https://doi.org/10.1109/ICIC53490.2021.9709625
Sharma KDubey SGaurav PPrachi (2020)Functionality Assessment of Software System using Fuzzy Approach2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO48877.2020.9197795(1206-1209)Online publication date: Jun-2020
https://doi.org/10.1109/ICRITO48877.2020.9197795
Saini GPanwar DKumar SSingh V(2020)A systematic literature review and comparative study of different software quality modelsJournal of Discrete Mathematical Sciences and Cryptography10.1080/09720529.2020.174718823:2(585-593)Online publication date: 14-May-2020
https://doi.org/10.1080/09720529.2020.1747188
Malhotra RLata K(2020)A systematic literature review on empirical studies towards prediction of software maintainabilitySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05005-424:21(16655-16677)Online publication date: 28-May-2020
https://dl.acm.org/doi/10.1007/s00500-020-05005-4
Shafiabady AAbdullah S(2018)Qualitative Evaluation on Software Maintainability Prediction Models2018 IEEE Student Conference on Research and Development (SCOReD)10.1109/SCORED.2018.8711151(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/SCORED.2018.8711151
Zhang WHuang LNg VGe J(2015)SMPLearnerAutomated Software Engineering10.1007/s10515-014-0161-322:1(111-141)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1007/s10515-014-0161-3
Malhotra RRaje R(2014)An empirical comparison of machine learning techniques for software defect predictionProceedings of the 8th International Conference on Bioinspired Information and Communications Technologies10.4108/icst.bict.2014.257871(320-327)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.4108/icst.bict.2014.257871
Sefid-Dashti BHabibi J(2014)A Reference Architecture for Mobile SOASystems Engineering10.1111/sys.2127917:4(407-425)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1111/sys.21279
Singh PSangwan O(2014)A process metrics based framework for Aspect Oriented Software to predict software bugs and maintenance2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence)10.1109/CONFLUENCE.2014.6949323(831-836)Online publication date: Sep-2014
https://doi.org/10.1109/CONFLUENCE.2014.6949323
Rai DTyagi K(2013)Estimating the regression test case selection probability using fuzzy rules2013 International Conference on Recent Trends in Information Technology (ICRTIT)10.1109/ICRTIT.2013.6844270(603-611)Online publication date: Jul-2013
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