Abstract

Constructive Cost Model II (COCOMO II) is investigated as the most popular model for software cost estimation. COCOMO II depends on several variables or Cost Drivers (CD). This research investigates the role of Effort Multiplier (EM) and Line of Code (LOC) to improve the accuracy of cost estimation. Fuzzy Logic has been implemented to the COCOMO II to represent the EM. Furthermore, in order to produce better estimation, this research uses Gaussian Membership Function to redesign the Effort Multiplier by studying the behavior of COCOMO II. This research also applies Neural Network (NN) approach to increase the accuracy of software effort estimation by training the software development datasets. The result is proposed model gives contribution to decrease error significantly.

Original languageEnglish
Title of host publicationProceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages197-202
Number of pages6
ISBN (Electronic)9781509000951
DOIs
Publication statusPublished - 12 Jan 2016
EventInternational Conference on Information and Communication Technology and Systems, ICTS 2015 - Surabaya, Indonesia
Duration: 16 Sept 2015 → …

Publication series

NameProceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015

Conference

ConferenceInternational Conference on Information and Communication Technology and Systems, ICTS 2015
Country/TerritoryIndonesia
CitySurabaya
Period16/09/15 → …

Keywords

  • COCOMO
  • Gaussian membership
  • cost driver
  • fuzzy
  • neural networks
  • software cost estimation

Fingerprint

Dive into the research topics of 'Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model'. Together they form a unique fingerprint.

Cite this