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

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Title:Machine Learning for Software Engineering: a Bibliometric Analysis from 2015 to 2019
Authors:Heradio, Ruben
Fernandez-Amoros, David
Cerrada, Cristina
Cobo, Manuel J.
Keywords:Soft Computing: Theory Innovations and Problem Solving Benefits
bibliometrics
machine learning
scientometrics
software engineering
Date Issued:05 Jan 2021
Abstract:The 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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70847
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.235
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Soft Computing: Theory Innovations and Problem Solving Benefits


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