IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i4d10.1007_s13198-021-01519-8.html
   My bibliography  Save this article

Incorporating whale optimization algorithm with deep belief network for software development effort estimation

Author

Listed:
  • Anupama Kaushik

    (Maharaja Surajmal Institute of Technology)

  • Niyati Singal

    (Maharaja Surajmal Institute of Technology)

  • Malvika Prasad

    (Maharaja Surajmal Institute of Technology)

Abstract

The software industry is highly competitive, and hence, it is imperative to have an accurate method to estimate the effort needed in the key phases of software development. Accurate estimates ensure efficient allocation of human and machine resources for the project. This paper proposes a technique for software development effort estimation using deep belief network (DBN). For fine-tuning of DBN, Whale Optimization Algorithm (WOA) is used which mimics the social behaviour of humpback whales. The proposed technique DBN-WOA has been experimentally evaluated on four promise datasets—COCOMO81, NASA93, MAXWELL and CHINA. The results from DBN-WOA are compared with the results from fine-tuning of DBN with backpropagation (DBN-BP) and it is observed that the proposed technique outscores DBN-BP. The proposed approach is also empirically validated through a statistical framework.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01519-8
    DOI: 10.1007/s13198-021-01519-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01519-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01519-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. B. Tirimula Rao & Satchidananda Dehuri & Rajib Mall, 2012. "Functional Link Artificial Neural Networks for Software Cost Estimation," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 3(2), pages 62-82, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Ajit Kumar Behera & Mrutyunjaya Panda & Satchidananda Dehuri, 2021. "Software reliability prediction by recurrent artificial chemical link network," 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. 12(6), pages 1308-1321, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01519-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.