Abstract

Due to its capacity to increase capital accuracy, Constructive Cost Model II (COCOMO II) is frequently chosen for predicting the cost of software projects. The accuracy level is frequently impacted by the large error value difference between COCOMO II and the real project cost. This problem can be improved by various optimization methods, such as BCO, ANN, Fuzzy, ACO, Cuckoo, and Grey Wolf optimization (GWO). Therefore, this study aimed to comparatively analyze the COCOMO II model for cost estimation. In this case, the implemented datasets were Nasa 93 and Turkish. In comparison to other optimization techniques, the results showed that COCOMO II-GWO with Fuzzy Gaussian reduced the outputs of MMRE by more than 16%. This subsequently led to the improvement of project cost-estimate accuracy levels.

Original languageEnglish
Title of host publicationProceedings - 13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-231
Number of pages6
ISBN (Electronic)9798350323184
DOIs
Publication statusPublished - 2023
Event13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023 - Penang, Malaysia
Duration: 25 Aug 202326 Aug 2023

Publication series

NameProceedings - 13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

Conference

Conference13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023
Country/TerritoryMalaysia
CityPenang
Period25/08/2326/08/23

Keywords

  • COCOMO II
  • GWO
  • MMRE
  • cost
  • software project

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