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Using Text Mining for Research Trends in Empirical Software Engineering

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Date

2021

Journal Title

Journal ISSN

Volume Title

Publisher

Gazi Univ

Open Access Color

GOLD

Green Open Access

No

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No
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Average
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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

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

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OpenCitations Citation Count
3

Source

Politeknik Dergisi

Volume

24

Issue

3

Start Page

1227

End Page

1235
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CrossRef : 1

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Mendeley Readers : 5

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