Optimizing Effort and Cost Estimation: Model Implementation Using Artificial Neural Networks and Taguchi’s Orthogonal Vector Plans

Nevena Ranković, Dragica Rankovic, Mirjana Ivanovic*, Ljubomir Lazic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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Abstract

Part 2 of this book consists of one large chapter focused on optimizing effort and cost estimation through Artificial Neural Networks (ANN) and Taguchi’s Orthogonal Vector Plans, which has been the main area of our exploration and research over the last decade. The chapter presents a novel methodology that enhances conventional models like COCOMO2000, COSMIC FFP, and UCP, improving their accuracy and efficiency. Through detailed analysis and comparisons, it demonstrates how AI-driven techniques and advanced optimization methods lead to more precise and scalable software project estimation.
Original languageEnglish
Title of host publicationRecent Advances in Artificial Intelligence in Cost Estimation in Project Management
EditorsNevena Ranković, Dragica Rankovic, Mirjana Ivanovic, Ljubomir Lazic
PublisherSpringer Cham
Chapter9
Pages291-417
Number of pages127
Edition1
ISBN (Electronic)978-3-031-76572-8
ISBN (Print)978-3-031-76571-1
DOIs
Publication statusPublished - 2024

Publication series

NameArtificial Intelligence-Enhanced Software and Systems Engineering
PublisherSpringer Cham
Volume6
ISSN (Print)2731-6025
ISSN (Electronic)2731-6033

Keywords

  • Taguchi orthogonal arrays
  • Optimization
  • Effort and cost estimation
  • Ensemble models

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