Integrating Chicken Swarm Optimization with Deep Learning for Software Effort Estimation

DOI

10.2991/978-94-6463-805-9_21How to use a DOI?

Keywords

Software effort estimation; Chicken swarm optimization; feed-forward deep neural network

Abstract

Achieving accurate estimation remains a major challenge and a key research topic in the software industry. There is an increasing use of Deep Learning (DL) in the field of software effort estimation. In this paper the Chicken Swarm Optimization (CSO) algorithm is applied to a feed-forward deep neural network (FFDNN) to optimize the connection weights and biases for software effort estimation projects. The proposed model is validated using four datasets. The performance of optimized FFDNN is evaluated using Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE) and Prediction (Pred) evaluation metrics. Experimental results show that tuning FFDNN by the CSO algorithm outperforms previous works presented in the literature.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fatima Zohra Laboudi
AU  - Kamilia Menghour
AU  - Labiba Souici-Meslati
PY  - 2025
DA  - 2025/08/05
TI  - Integrating Chicken Swarm Optimization with Deep Learning for Software Effort Estimation
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 185
EP  - 193
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_21
DO  - 10.2991/978-94-6463-805-9_21
ID  - Laboudi2025
ER  -