Detecting Latent Topics and Trends in Software Engineering Research Since 1980 Using Probabilistic Topic Modeling

Autor: Fatih Gurcan, Gonca Gokce Menekse Dalveren, Nergiz Ercil Cagiltay, Ahmet Soylu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IEEE Access, Vol 10, Pp 74638-74654 (2022)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2022.3190632
Popis: The landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends. Therefore, today’s research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field. From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years. This study first presents an objective and systematic method for corpus creation through major publication sources in the field. A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019. Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach. As a result of this analysis, 24 main topics were found. In addition, topical trends in the field were revealed. Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.
Databáze: Directory of Open Access Journals