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A tuned feed-forward deep neural network algorithm for effort estimation.
- Source :
- Journal of Experimental & Theoretical Artificial Intelligence; Apr2022, Vol. 34 Issue 2, p235-259, 25p
- Publication Year :
- 2022
-
Abstract
- Software effort estimation (SEE) is a software engineering problem that requires robust predictive models. To establish robust models, the most feasible configuration of hyperparameters of regression methods is searched. Although only a few works, which include hyperparameter optimisation (HO), have been done so far for SEE, there is not any comprehensive study including deep learning models. In this study, a feed-forward deep neural network algorithm (FFDNN) is proposed for software effort estimation. The algorithm relies on a binary-search-based method for finding hyperparameters. FFDNN outperforms five comparison algorithms in the experiment that uses two performance parameters. The results of the study suggest that: 1) Employing traditional methods such as grid and random search increases tuning time remarkably. Instead, sophisticated parameter search methods compatible with the structure of regression method should be developed; 2) The performance of SEE is enhanced when associated hyperparameter search method is devised according to the essentials of chosen deep learning approach; 3) Deep learning models achieve in competitive CPU time compared to the tree-based regression methods such as CART_DE8. [ABSTRACT FROM AUTHOR]
- Subjects :
- SOFTWARE engineering
ALGORITHMS
DEEP learning
SOFTWARE engineers
PREDICTION models
Subjects
Details
- Language :
- English
- ISSN :
- 0952813X
- Volume :
- 34
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Experimental & Theoretical Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 156246139
- Full Text :
- https://doi.org/10.1080/0952813X.2021.1871664