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Software cost optimization integrating fuzzy system and COA-Cuckoo optimization algorithm

Author

Listed:
  • Anupama Kaushik

    (Maharaja Surajmal Institute of Technology)

  • Shivi Verma

    (Maharaja Surajmal Institute of Technology)

  • Harsh Jot Singh

    (Maharaja Surajmal Institute of Technology)

  • Gitika Chhabra

    (Maharaja Surajmal Institute of Technology)

Abstract

Processing the uncertainty in the software cost estimation is now highly needful considering the growing use of software cost optimization in modern organizations. In this paper, a new methodology for software cost optimization is introduced. The software cost estimation is an “approximate judgment” of the cost and effort incurred in a software project model. The proposed method (CUCKOO-FIS) integrates two optimization techniques-Cuckoo optimization, a meta-heuristic search algorithm and Fuzzy Inference System, a mathematical system based on fuzzy logic. The collaborated technique is applied to software cost estimation model for effort optimization and is successfully evaluated on the tera-PROMISE datasets. Many model based methods have been proposed earlier but this estimation using Cuckoo algorithm and Fuzzy sets which runs on non-algorithmic methods have showed results with improved accuracy in cost estimation.

Suggested Citation

  • Anupama Kaushik & Shivi Verma & Harsh Jot Singh & Gitika Chhabra, 2017. "Software cost optimization integrating fuzzy system and COA-Cuckoo optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1461-1471, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-017-0615-7
    DOI: 10.1007/s13198-017-0615-7
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    References listed on IDEAS

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    1. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
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    Cited by:

    1. Shweta Singhal & Nishtha Jatana & Kavita Sheoran & Geetika Dhand & Shaily Malik & Reena Gupta & Bharti Suri & Mudligiriyappa Niranjanamurthy & Sachi Nandan Mohanty & Nihar Ranjan Pradhan, 2023. "Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP," Mathematics, MDPI, vol. 11(13), pages 1-21, July.
    2. Abdul Latif & Arup Pramanik & Dulal Chandra Das & Israfil Hussain & Sudhanshu Ranjan, 2018. "Plug in hybrid vehicle-wind-diesel autonomous hybrid power system: frequency control using FA and CSA optimized controller," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1147-1158, October.
    3. Anupama Kaushik & Niyati Singal & Malvika Prasad, 2022. "Incorporating whale optimization algorithm with deep belief network for software development effort estimation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1637-1651, August.

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