Akademik Veri Yönetim Sistemi
IEEE Access, 2026 (SCI-Expanded, Scopus)
This study addresses the critical role of developer profile modeling in today's dynamic software industry. Following mapping study guidelines, we reviewed 254 primary studies from five databases, and analyzed them against five research questions to report profile definitions, relevant software engineering roles, data sources, measures to quantify the profiles, and tasks used for empirical analysis. Our analysis highlights the need for standardized profile definitions due to many characteristics, such as gender, expertise, behavior, activity, chosen in the literature. The most frequently analyzed people are software developers, particularly for their role in coding. A notable trend is using profile modeling to characterize developers, focusing on personality traits, gender, and skills, and through mining data from GitHub. We also identified an inevitable research direction that revolves around developer profiling, particularly regarding interactions with Large Language Model agents. This sharing behavior can foster diverse profile characteristics and lead to the development of quantifiable metrics for profile definitions in artificial intelligence-assisted contexts. We present a mapping between profile definitions, data types (industry versus academia), metric groups to quantify these definitions, and their applications in two software engineering tasks. Our analysis suggests the need for future studies to define more generic profile modeling through multiple characteristics, better quantification of these characteristics through independent measures, and more analysis on different software engineer roles, such as testers. Exploring the sharing behaviors associated with artificial intelligence and Large Language Model agents will significantly enhance developer profiling by promoting the adoption of artificial intelligence-assisted software development.