Bibliometric and Content Analysis of Large Language Models Research in Software Engineering: The Potential and Limitation in Software Engineering

Guardado en:
Detalles Bibliográficos
Publicado en:International Journal of Advanced Computer Science and Applications vol. 16, no. 4 (2025)
Autor principal: PDF
Publicado:
Science and Information (SAI) Organization Limited
Materias:
Acceso en línea:Citation/Abstract
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nab a2200000uu 4500
001 3206239580
003 UK-CbPIL
022 |a 2158-107X 
022 |a 2156-5570 
024 7 |a 10.14569/IJACSA.2025.0160436  |2 doi 
035 |a 3206239580 
045 2 |b d20250101  |b d20251231 
100 1 |a PDF 
245 1 |a Bibliometric and Content Analysis of Large Language Models Research in Software Engineering: The Potential and Limitation in Software Engineering 
260 |b Science and Information (SAI) Organization Limited  |c 2025 
513 |a Journal Article 
520 3 |a Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings International Conference on Software Engineering and ACM International Conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. 
610 4 |a OpenAI 
651 4 |a China 
653 |a Data analysis 
653 |a Journals 
653 |a Engineering research 
653 |a Large language models 
653 |a Engineering education 
653 |a Artificial neural networks 
653 |a Content analysis 
653 |a Knowledge management 
653 |a International conferences 
653 |a Language 
653 |a Software development 
653 |a Computer science 
653 |a Bibliometrics 
653 |a Trends 
653 |a Publications 
653 |a Public health 
653 |a Software engineering 
653 |a Education 
653 |a Chatbots 
773 0 |t International Journal of Advanced Computer Science and Applications  |g vol. 16, no. 4 (2025) 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3206239580/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3206239580/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch