@conference {Miloudi2022,
	title = {Maintenance Effort Estimation for Open Source Software: Current trends},
	booktitle = {CEUR Workshop Proceedings},
	volume = {3272},
	year = {2022},
	note = {cited By 0},
	abstract = {Software maintenance of Open Source Software (OSS) has gained more attention in recent years and facilitated by the help of the Internet. Since volunteers in OSS do not record the effort of their contribution in maintenance tasks, researchers have to indirectly estimate the maintenance effort of such software. A review of the published OSS-MEE models has been performed using a set of 65 selected studies in a Systematic Mapping Study (SMS). This study analyses, discusses the state of the art about O-MEE and identifies trends through five additional Mapping Questions (MQs). In summary, various maintenance effort estimation (MEE) models were developed for OSS or industrial software. Researchers have mostly expressed the maintenance effort in terms of bug fixing, bug resolution time and severity in conjunction with bug report attributes. Regression Analysis and Bayesian Networks were most used estimation techniques, Recall, Precision, R2 and F-measure evaluation criteria in addition to k-fold cross validation method. Most of the models were implemented using WEKA, R software and MATLAB. More than half of the selected studies lacked of any validity analysis of their results. Trends are also discussed to identify a set of implications for researchers. {\textcopyright} 2020 Copyright for this paper by its authors.},
	keywords = {{\textquoteright}current, Bayesian networks, Bug-fixing, Computer software maintenance, Effort Estimation, Effort estimation model, Industrial software, Maintenance efforts, Maintenance tasks, Mapping, MATLAB, Open source software, Open systems, Open-source softwares, Regression analysis, State of the art, Systematic mapping studies},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142872497\&partnerID=40\&md5=d5e99ad4babcbb3a4fd5f50001fae51c},
	author = {Miloudi, C. and Cheikhi, L. and Abran, A. and Idri, A.}
}
@article {Farih2021224,
	title = {Effort Estimation in Agile Software Development: A Systematic Mapping Study},
	journal = {Frontiers in Artificial Intelligence and Applications},
	volume = {337},
	year = {2021},
	note = {cited By 0},
	pages = {224-234},
	abstract = {Context: Estimating effort has always been considered an important element at the start of each software development project. The challenge of estimating the effort of software development lies in its precision. With the emergence of agile methodologies, methods for effort estimation (EE) had to adapt to this new development path. In this article, we are conducting a systematic mapping study on effort estimation in the context of agile software development. Objective: we want to identify the estimation approaches and techniques used in the context of agile development to better understand the specifics and trends relating to this mode of development. Method: we conducted a systematic mapping study by adopting the guideline explained in[1] [2]. A systematic review of the literature [3] has already been carried out for publications between 2001 and 2013. This work is an extension of this previous study. We queried 5 electronic databases. Conclusion: We retrieved 11350 paper from five electronic databases. A total of 108 papers is selected after applying the inclusion and exclusion criteria. Based on the results, there is a general increase over the years of studies concerning effort estimation in agile software development. {\textcopyright} 2021 The authors and IOS Press. All rights reserved.},
	keywords = {Agile Methodologies, Agile software development, Development path, Effort Estimation, Electronic database, Estimation approaches, Estimation techniques, Mapping, Metric, Software design, Software development projects, Systematic mapping studies},
	doi = {10.3233/FAIA210022},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116486825\&doi=10.3233\%2fFAIA210022\&partnerID=40\&md5=e5d5c11a77c1e5c926d09163394e07d8},
	author = {Farih, N.E. and Nafil, K. and El Messousi, R.}
}
@article {Hosni2021,
	title = {On the value of filter feature selection techniques in homogeneous ensembles effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {33},
	number = {6},
	year = {2021},
	note = {cited By 1},
	abstract = {Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the features that feed the ML technique, selecting the appropriate features in the preprocessing data step is important. This paper investigates three filter feature selection techniques to check the predictive capability of four single ML techniques: K-nearest neighbor, support vector regression, multilayer perceptron, and decision trees and their homogeneous ensembles over six well-known datasets. Furthermore, the single and ensembles techniques were optimized using the grid search optimization method. The results suggest that the three filter feature selection techniques investigated improve the reasonability and the accuracy performance of the four single techniques. Moreover, the homogeneous ensembles are statistically more accurate than the single techniques. Finally, adopting a random process (i.e., random subspace method) to select the inputs feature for ML technique is not always effective to generate an accurate homogeneous ensemble. {\textcopyright} 2021 John Wiley \& Sons, Ltd.},
	keywords = {Decision trees, Effort Estimation, Feature extraction, K-nearest neighbors, Multilayer neural networks, Nearest neighbor search, Object oriented programming, Predictive capabilities, Project management, Random processes, Random subspace method, Selection techniques, Software design, Software development effort, Software project management, Software systems, Support vector regression},
	doi = {10.1002/smr.2343},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103402056\&doi=10.1002\%2fsmr.2343\&partnerID=40\&md5=0f4c04b547f3d628d6db8d65b74912e5},
	author = {Hosni, M. and Idri, A. and Abran, A.}
}
