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This study explores the behavioral patterns of software engineers using Large Language Models (LLMs) in their development workflows, revealing a trend towards functional dependence on these tools for tasks like code generation and debugging. Through an exploratory survey of 119 practitioners, the research identifies signs of overreliance, where engineers prioritize LLM outputs over traditional resources like documentation and peer consultation, while addiction-related behaviors were less frequently reported. The findings highlight the need for promoting balanced use of LLMs to ensure trust calibration and professional judgment in software engineering practices.
Software engineers are increasingly dependent on LLMs, risking overreliance that could undermine traditional practices like peer consultation and documentation.
The widespread adoption of Large Language Models (LLMs) has changed how software engineers perform everyday development activities. While these systems provide substantial support for tasks such as code generation, debugging, and documentation, their increasing integration into professional workflows has also raised questions regarding developers'reliance on these tools and the emergence of dependence, overreliance, and addiction-related behaviors. This study investigates how software engineers experience the use of LLMs during professional software development, with attention to behavioral patterns associated with dependence, overreliance, and addiction-related behaviors. An exploratory survey was conducted with 119 software practitioners. The data were analyzed using descriptive statistics and qualitative thematic analysis of participants'open-ended responses. Participants primarily described functional dependence, with LLMs becoming integrated into routine software engineering activities because of the productivity and efficiency they provide. Responses also suggested patterns consistent with overreliance, particularly through prioritizing LLMs over documentation or peer consultation while continuing to verify generated outputs. Reports associated with addiction-related behaviors were less common and primarily reflected difficulty moderating use or emotional attachment to the technology rather than impaired control. The findings suggest that LLMs are becoming a habitual component of professional software engineering practice. While most reported use appears functional, the results indicate the importance of promoting appropriate reliance by supporting trust calibration, professional judgment, and verification throughout software development.
