Model-Assisted and Human-Guided: Perceptions and Practices of Software Professionals Using LLMs for Coding

Large Language Models have quickly become a central component of modern software development workflows, and software practitioners are increasingly integrating LLMs into various stages of the software development lifecycle. Despite the growing presence of LLMs, there is still a limited understanding of how these tools are actually used in practice and how professionals perceive their benefits and limitations. This paper presents preliminary findings from a global survey of 131 software practitioners. Our results reveal how LLMs are utilized for various coding-specific tasks. Software professionals report benefits such as increased productivity, reduced cognitive load, and faster learning, but also raise concerns about LLMs' inaccurate outputs, limited context awareness, and associated ethical risks. Most developers treat LLMs as assistive tools rather than standalone solutions, reflecting a cautious yet practical approach to their integration. Our findings provide an early, practitioner-focused perspective on LLM adoption, highlighting key considerations for future research and responsible use in software engineering.