Neural Networks based Software Development Effort Estimation: A Systematic Mapping Study

Fatima Boujida, Fatima Amazal, Ali Idri

2021

Abstract

Developing an efficient model that accurately predicts the development effort of a software project is an important task in software project management. Artificial neural networks (ANNs) are promising for building predictive models since their ability to learn from previous data, adapt and produce more accurate results. In this paper, we conducted a systematic mapping study of papers dealing with the estimation of software development effort based on artificial neural networks. In total, 80 relevant studies were identified between 1993 and 2020 and classified with respect to five criteria: publication source, research approach, contribution type, techniques used in combination with ANN models and type of the neural network used. The results showed that, most ANN-based software development effort estimation (SDEE) studies applied the history-based evaluation (HE) and solution proposal (SP) approaches. Besides, the feedforward neural network was the most frequently used ANN type among SDEE researchers. To improve the performance of ANN models, most papers employed optimization methods such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) in combination with ANN models.

Paper Citation

in Harvard Style

Boujida F., Amazal F. and Idri A. (2021). Neural Networks based Software Development Effort Estimation: A Systematic Mapping Study. In Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-523-4, pages 102-110. DOI: 10.5220/0010603701020110

in Bibtex Style

@conference{icsoft21,
author={Fatima Boujida and Fatima Amazal and Ali Idri},
title={Neural Networks based Software Development Effort Estimation: A Systematic Mapping Study},
booktitle={Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2021},
pages={102-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010603701020110},
isbn={978-989-758-523-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Neural Networks based Software Development Effort Estimation: A Systematic Mapping Study
SN - 978-989-758-523-4
AU - Boujida F.
AU - Amazal F.
AU - Idri A.
PY - 2021
SP - 102
EP - 110
DO - 10.5220/0010603701020110