A Bibliometric Mapping of AI-Based Assistants in Software Engineering Using Bibliometrix and the Scopus Dataset (2014–2025)

Abstract

Artificial intelligence-based assistants have been developing at an incredible pace in recent years, facilitating fast information retrieval, automating repetitive tasks, and improving operational efficiency. In the field of software engineering, these tools now assist in activities such as testing, code generation, and documentation. Despite the increasing recognition of artificial intelligence (AI) as a valuable element of software development, its overall contributions remain insufficiently characterized. This research conducts a bibliometric analysis of AI-based assistants in the context of software development, identifying key publications, leading authors and organizations, and underexplored areas where these tools have a significant impact. As a result of a literature search conducted using the Scopus database, 83 papers (2014–2025) were analyzed using the Bibliometrix R-package for bibliometric evaluation. The collected documents revealed a sustained annual growth rate of 24.14%, with a peak in 2024 (31 papers), reflecting the surge in generative AI and LLM-based tools. The most cited article received 118 citations (39.33 citations per year), highlighting a strong impact in recent contributions. The United States led in publications with 26.1%, while Europe had the highest citation impact with 73 citations. Software design is the dominant theme with 41 papers and 19% of occurrences, while keyword trends focus on language models (LLM) with 19 papers, chatbots with 18 papers, and LLMs with 11 papers, confirming a clear shift toward LLM-centered research in software engineering. It provides insights into future research directions and opportunities for AI-driven innovation in software development .