Exploring Dependence, Overreliance, and Addiction Related Behaviors Associated with Large Language Model Use Among Software Engineers

2026-08-05

AI Summary

This study systematically identifies and differentiates functional dependency, overreliance, and addiction-like behaviors in the use of large language models (LLMs) within software engineering. Through a survey of 119 software practitioners, complemented by descriptive statistics and thematic analysis of open-ended responses, the research reveals that LLMs have become deeply integrated into development workflows. Most developers exhibit functional dependency, leveraging LLMs as productive tools without impairment. Overreliance manifests as a preference for consulting LLMs over official documentation or colleagues, potentially compromising solution quality. Addiction-like behaviors are relatively rare but characterized by difficulties in usage moderation and emotional attachment. The findings provide an empirical foundation and a behavioral classification framework for understanding the nuanced impacts of LLM adoption in software development.

Abstract

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.

Problem

• dependence
• overreliance
• addiction-related behaviors
• Large Language Models
• software engineers

Innovation

• Large Language Models
• Software Engineering
• Technology Dependence
• Overreliance
• Human-AI Interaction