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Empirical Data Modeling in Software Engineering Using Radial Basis Functions
June 2000 (vol. 26 no. 6)
pp. 567-576

Abstract—Many empirical studies in software engineering involve relationships between various process and product characteristics derived via linear regression analysis. In this paper, we propose an alternative modeling approach using Radial Basis Functions (RBFs) which provide a flexible way to generalize linear regression function. Further, RBF models possess strong mathematical properties of universal and best approximation. We present an objective modeling methodology for determining model parameters using our recent SG algorithm, followed by a model selection procedure based on generalization ability. Finally, we describe a detailed RBF modeling study for software effort estimation using a well-known NASA dataset.

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Index Terms:
Empirical modeling, radial basis functions, data analysis, software effort estimation.
Miyoung Shin, Amrit L. Goel, "Empirical Data Modeling in Software Engineering Using Radial Basis Functions," IEEE Transactions on Software Engineering, vol. 26, no. 6, pp. 567-576, June 2000, doi:10.1109/32.852743
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