Using Text Mining for Research Trends in Empirical Software Engineering
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Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Gazi Univ
Open Access Color
GOLD
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
This paper intends to examine the research trends in Empirical Software Engineering domain within the last two decades using text mining. It studies published articles in the relevant literature with an emphasis on abstracts of 10658 articles published in the literature on Experimental Software Engineering domain. Using a probabilistic topic modelling technique (Latent Dirichlet Allocation), it brings forward the main topics of research within this domain. By further analysis, the paper evaluates the changes of focus in published works in the last two decades and depicts the recent trends in research content wise. Through a timely comparison, it portrays the alteration of interest within empirical software engineering research and proposes a future research agenda to develop an advanced field, beneficial both for academics and practitioners.
Description
Tokdemir, Gul/0000-0003-2441-3056
ORCID
Keywords
Topic Modelling, Research Trends, Latent Dirichlet Allocation, Empirical Software Engineering, Engineering, Empirical software engineering;topic modelling;research trends;latent dirichlet allocation, Mühendislik, Deneysel yazılım mühendisliği;konu modelleme;araştırma eğilimleri;latent dirichlet allocation
Fields of Science
0508 media and communications, 0502 economics and business, 05 social sciences
Citation
Tokdemir, Gül (2021). "Using text mining for research trends in empirical software engineering ", Politeknik Dergisi, Vol. 24, No. 3, pp. 1227-1235.
WoS Q
Q4
Scopus Q

OpenCitations Citation Count
3
Source
Politeknik Dergisi
Volume
24
Issue
3
Start Page
1227
End Page
1235
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Citations
CrossRef : 1
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Mendeley Readers : 5
Web of Science™ Citations
1
checked on Feb 23, 2026
Page Views
5
checked on Feb 23, 2026
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