dc.contributor.author | Fatih, Gurcan | |
dc.contributor.author | Gonca Gokce Menekse, Dalveren | |
dc.contributor.author | Nergiz, Ercil Cagiltay | |
dc.contributor.author | Soylu, Ahmet | |
dc.date.accessioned | 2023-01-18T12:47:05Z | |
dc.date.available | 2023-01-18T12:47:05Z | |
dc.date.created | 2022-07-22T23:03:19Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | IEEE Access. 2022, 10 74638-74654. | en_US |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | https://hdl.handle.net/11250/3044317 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Detecting Latent Topics and Trends in Software Engineering Research Since 1980 Using Probabilistic Topic Modeling | en_US |
dc.title.alternative | Detecting Latent Topics and Trends in Software Engineering Research Since 1980 Using Probabilistic Topic Modeling | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 74638-74654 | en_US |
dc.source.volume | 10 | en_US |
dc.source.journal | IEEE Access | en_US |
dc.identifier.doi | 10.1109/ACCESS.2022.3190632 | |
dc.identifier.cristin | 2039164 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |