Machine Learning for Software Engineering: a Bibliometric Analysis from 2015 to 2019

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dc.contributor.authorHeradio, Ruben
dc.contributor.authorFernandez-Amoros, David
dc.contributor.authorCerrada, Cristina
dc.contributor.authorCobo, Manuel J.
dc.date.accessioned2020-12-24T19:22:57Z
dc.date.available2020-12-24T19:22:57Z
dc.date.issued2021-01-05
dc.description.abstractThe increase of computer processor speed and the ubiquitous availability of data coming from a diversity of sources (e.g., version control systems, software developers forums, operating system logs, etc.) have boosted the interest in applying machine learning to software engineering. Accordingly, the research literature on this topic has increased rapidly. This paper provides a comprehensive overview of that literature for the last five years. To do so, it examines 1,312 records gathered from Elsevier Scopus, identifying (i) the most productive authors and their collaboration networks, (ii) the countries and institutions that are leading research, (iii) the journals that are publishing the most papers, and (iv) the most important research themes and the highest impacted articles for those themes.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.235
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70847
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSoft Computing: Theory Innovations and Problem Solving Benefits
dc.subjectbibliometrics
dc.subjectmachine learning
dc.subjectscientometrics
dc.subjectsoftware engineering
dc.titleMachine Learning for Software Engineering: a Bibliometric Analysis from 2015 to 2019
prism.startingpage1928

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Soft Computing: Theory Innovations and Problem Solving Benefits