Authors:
Fatima Ezzahra Boujida
1
;
Fatima Azzahra Amazal
1
and
Ali Idri
2
Affiliations:
1
LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000 Agadir, Morocco
;
2
Software Projects Management Research Team, ENSIAS, Mohammed V University, Madinate Al Irfane, 10100 Rabat, Morocco
Keyword(s):
Systematic Mapping Study, Software Development Effort Estimation, Artificial Neural Networks.
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.
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