@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 {Hosni20212827,
	title = {A systematic mapping study for ensemble classification methods in cardiovascular disease},
	journal = {Artificial Intelligence Review},
	volume = {54},
	number = {4},
	year = {2021},
	note = {cited By 5},
	pages = {2827-2861},
	abstract = {Ensemble methods overcome the limitations of single machine learning techniques by combining different techniques, and are employed in the quest to achieve a high level of accuracy. This approach has been investigated in various fields, one of them being that of bioinformatics. One of the most frequent applications of ensemble techniques involves research into cardiovascular diseases, which are considered the leading cause of death worldwide. The purpose of this research work is to identify the papers that investigate ensemble classification techniques applied to cardiology diseases, and to analyse them according to nine aspects: their publication venues, the medical tasks tackled, the empirical and research types adopted, the types of ensembles proposed, the single techniques used to construct the ensembles, the validation frameworks adopted to evaluate the proposed ensembles, the tools used to build the ensembles, and the optimization methods employed for the single techniques. This paper reports the carrying out of a systematic mapping study. An extensive automatic search in four digital libraries: IEEE Xplore, ACM Digital Library, PubMed, and Scopus, followed by a study selection process, resulted in the identification of 351 papers that were used to address our mapping questions. This study found that the papers selected had been published in a large number of different resources. The medical task addressed most frequently by the selected studies was diagnosis. In addition, the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted by the selected studies. Homogeneous ensembles were the ensemble type that was developed most often in literature, while decision trees, artificial neural networks and Bayesian classifiers were the single techniques used most frequently to develop ensemble classification methods. The weighted majority and majority voting rules were adopted to obtain the final decision of the ensembles developed. With regard to evaluation frameworks, the datasets obtained from the UCI and PhysioBank repositories were those used most often to evaluate the ensemble methods, while the k-fold cross-validation method was the most frequently-employed validation technique. Several tools with which to build ensemble classifiers were identified, and the type of software adopted with the greatest frequency was open source. Finally, only a few researchers took into account the optimization of the parameter settings of either single or meta ensemble classifiers. This mapping study attempts to provide a greater insight into the application of ensemble classification methods in cardiovascular diseases. The majority of the selected papers reported positive feedback as regards the ability of ensemble methods to perform better than single methods. Further analysis is required to aggregate the evidence reported in literature. {\textcopyright} 2020, Springer Nature B.V.},
	keywords = {Bayesian networks, Cardio-vascular disease, Cardiology, Decision trees, Diagnosis, Digital libraries, Diseases, Ensemble classification, Ensemble classifiers, Evaluation framework, K fold cross validations, Learning systems, Majority voting rules, Mapping, Open source software, Open systems, Optimization method, Systematic mapping studies},
	doi = {10.1007/s10462-020-09914-6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091735819\&doi=10.1007\%2fs10462-020-09914-6\&partnerID=40\&md5=69ea4b02de420c3ec6a85e1f3c7dddaf},
	author = {Hosni, M. and Carrillo de Gea, J.M. and Idri, A. and El Bajta, M. and Fern{\'a}ndez Alem{\'a}n, J.L. and Garc{\'\i}a-Mateos, G. and Abnane, I.}
}
@conference {Miloudi2020251,
	title = {A Review of Open Source Software Maintenance Effort Estimation},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {251-256},
	abstract = {Open Source Software (OSS) is gaining interests of software engineering community as well as practitioners from industry with the growth of the internet. Studies in estimating maintenance effort (MEE) of such software product have been published in the literature in order to provide better estimation. The aim of this study is to provide a review of studies related to maintenance effort estimation for open source software (OSSMEE). To this end, a set of 60 primary empirical studies are selected from six electronic databases and a discussion is provided according to eight research questions (RQs) related to: publication year, publication source, datasets (OSS projects), metrics (independent variables), techniques, maintenance effort (dependent variable), validation methods, and accuracy criteria used in the empirical validation. This study has found that popular OSS projects have been used, Linear Regression, Na{\"\i}ve Bayes and k Nearest Neighbors were frequently used, and bug resolution was the most used regarding the estimation of maintenance effort for the future releases. A set of gaps are identified and recommendations for researchers are also provided. {\textcopyright} 2020 ACM.},
	keywords = {Computer software maintenance, Dependent variables, Electronic database, Empirical validation, Engineering community, Independent variables, Intelligent systems, K-nearest neighbors, Maintenance efforts, Nearest neighbor search, Open source software, Open systems, Research questions, Sodium compounds},
	doi = {10.1145/3419604.3419809},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096413214\&doi=10.1145\%2f3419604.3419809\&partnerID=40\&md5=7ad5b9eacb605d7875e2123cfec6ddeb},
	author = {Miloudi, C. and Cheikhi, L. and Idri, A.}
}
