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

Date
2021-01-05
Authors
Heradio, Ruben
Fernandez-Amoros, David
Cerrada, Cristina
Cobo, Manuel J.
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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.
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Soft Computing: Theory Innovations and Problem Solving Benefits, bibliometrics, machine learning, scientometrics, software engineering
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