Maintenance Effort Estimation for Open Source Software: Current trends

TitreMaintenance Effort Estimation for Open Source Software: Current trends
Publication TypeConference Paper
Year of Publication2022
AuthorsMiloudi, C, Cheikhi, L, Abran, A, Idri, A
Conference NameCEUR Workshop Proceedings
Mots-clés'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
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. © 2020 Copyright for this paper by its authors.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85142872497&partnerID=40&md5=d5e99ad4babcbb3a4fd5f50001fae51c
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