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

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Recent Advances in Artificial Intelligence in Cost Estimation in Project Management

Part of the book series: Artificial Intelligence-Enhanced Software and Systems Engineering ((AISSE,volume 6))

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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.

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Authors and Affiliations

  1. Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands

    Nevena Rankovic

  2. School of Computing, Department of Informatics, Union University, Belgrade, Serbia

    Dragica Ranković

  3. Faculty of Sciences, Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanovic

  4. School of Computing, Department of Computer Engineering, Union University, Belgrade, Serbia

    Ljubomir Lazić

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  1. Nevena Rankovic
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  2. Dragica Ranković
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  4. Ljubomir Lazić
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Correspondence to Mirjana Ivanovic .

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Rankovic, N., Ranković, D., Ivanovic, M., Lazić, L. (2024). Optimizing Effort and Cost Estimation: Model Implementation Using Artificial Neural Networks and Taguchi’s Orthogonal Vector Plans. In: Recent Advances in Artificial Intelligence in Cost Estimation in Project Management. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-031-76572-8_9

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