@article {Amazal2021,
	title = {Estimating software development effort using fuzzy clustering-based analogy},
	journal = {Journal of Software: Evolution and Process},
	volume = {33},
	number = {4},
	year = {2021},
	note = {cited By 3},
	abstract = {During the past decades, many studies have been carried out in an attempt to build accurate software development effort estimation techniques. However, none of the techniques proposed has proven to be successful at predicting software effort in all circumstances. Among these techniques, analogy-based estimation has gained significant popularity within software engineering community because of its outstanding performance and ability to mimic the human problem solving approach. One of the challenges facing analogy-based effort estimation is how to predict effort when software projects are described by a mixture of continuous and categorical features. To address this issue, the present study proposes an improvement of our former 2FA-kprototypes technique referred to as 2FA-cmeans. 2FA-cmeans uses a clustering technique, called general fuzzy c-means, which is a generalization of the fuzzy c-means clustering technique to cluster objects with mixed attributes. The performance of 2FA-cmeans was evaluated and compared with that of our former 2FA-kprototypes technique as well as classical analogy over six datasets that are quite diverse and have different sizes. Empirical results showed that 2FA-cmeans outperforms the two other analogy techniques using both all-in and jackknife evaluation methods. This was also confirmed by the win{\textendash}tie{\textendash}loss statistics and the Scott{\textendash}Knott test. {\textcopyright} 2020 John Wiley \& Sons, Ltd.},
	keywords = {Analogy based estimation, Categorical features, Cluster analysis, Clustering techniques, Engineering community, Estimating software, Fuzzy C means clustering, Fuzzy clustering, Fuzzy systems, Human problem solving, Software design, Software development effort},
	doi = {10.1002/smr.2324},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092446413\&doi=10.1002\%2fsmr.2324\&partnerID=40\&md5=76da5a67bd24c945c3aa985d13dad134},
	author = {Amazal, F.A. and Idri, A.}
}
@conference {Boujida2021102,
	title = {Neural networks based software development effort estimation: A systematic mapping study},
	booktitle = {Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021},
	year = {2021},
	note = {cited By 0},
	pages = {102-110},
	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. Copyright {\textcopyright} 2021 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved},
	keywords = {Ann models, Feedforward neural networks, Genetic algorithms, Mapping, Optimization method, Particle swarm optimization (PSO), Predictive analytics, Predictive models, Project management, Research approach, Software design, Software development effort, Software project, Software project management, Systematic mapping studies},
	doi = {10.5220/0010603701020110},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111770639\&doi=10.5220\%2f0010603701020110\&partnerID=40\&md5=c4592c4b704daf3823eb5e9e3e1e5693},
	author = {Boujida, F.E. and Amazal, F.A. and Idri, A.}
}
@conference {Idri2012863,
	title = {Software cost estimation by fuzzy analogy for ISBSG repository},
	booktitle = {World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.},
	volume = {7},
	year = {2012},
	note = {cited By 0},
	pages = {863-868},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892659716\&partnerID=40\&md5=2001b51ce67b133f56f1ca779fc56611},
	author = {Idri, A. and Amazal, F.A.}
}
