A data-driven cost estimation model for agile development based on Kolmogorov-Arnold Networks and AdamW optimization

Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However, accurate cost estimation in agile projects remains challenging, mainly due to frequent requirement changes and data scarcity. Traditional estimatio...

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Vydáno v:Journal of King Saud University. Computer and information sciences Ročník 37; číslo 5; s. 85 - 21
Hlavní autoři: Zhao, Xiaoyan, Xiong, Xin, Mansor, Zulkefli, Razali, Rozilawati, Ahmad Nazri, Mohd Zakree, Li, Liangyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.07.2025
Springer Nature B.V
Springer
Témata:
Accuracy
Adaptive Moment Estimation with Weight Decay algorithm
Agile development
Algorithms
Artificial intelligence
Artificial neural networks
Computer Imaging
Computer Science
Cost estimation
Database Management
Function points
Kolmogorov-Arnold Networks
Machine Learning
Modelling
Original Paper
Pattern Recognition and Graphics
Polynomials
Research methodology
Software development
Software engineering
Software Engineering/Programming and Operating Systems
Stability
Systems and Data Security
Theory of Computation
Vision
Adaptive Moment Estimation with Weight Decay algorithm
Cost estimation
Kolmogorov-Arnold Networks
Agile development
ISSN:1319-1578, 2213-1248, 1319-1578
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Popis
Shrnutí:Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However, accurate cost estimation in agile projects remains challenging, mainly due to frequent requirement changes and data scarcity. Traditional estimation methods and existing machine learning models often fail to adapt effectively to the dynamic agile environment. To address these issues, this paper proposes a novel cost estimation model combining Kolmogorov-Arnold Networks (KAN) with the AdamW optimizer. KAN captures complex nonlinear relationships through hierarchical polynomial mapping, making it suitable for modeling dynamic cost variations. AdamW improves convergence speed and stability with adaptive learning rates and momentum mechanisms. To alleviate data scarcity, the SMOTE-NC technique is applied to generate 1000 synthetic samples based on 75 actual agile project data. K-fold cross-validation is used to enhance the model’s generalization ability. Experimental results demonstrate that the proposed KAN-AdamW model achieves superior performance, with a Mean Absolute Error (MAE) of 11,504.08 and a Mean Relative Error (MRE) of 0.12-outperforming traditional Artificial Neural Networks (ANN) and function point-based models. The model also shows strong performance in accuracy and R-squared metrics, indicating high predictive stability and precision. This study offers a data-driven and effective solution for agile cost estimation and provides empirical support for addressing data limitations using SMOTE-NC. Furthermore, it highlights the potential of KAN for broader applications in cost modeling.
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ISSN:1319-1578
2213-1248
1319-1578
DOI:10.1007/s44443-025-00058-7