@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.}
}
