Understanding the Role of Large Language Models in Software Engineering: Evidence from an Industry Survey

TL;DR

This paper presents an empirical industry survey on how Large Language Models are adopted in software engineering, highlighting benefits like faster problem-solving and documentation, alongside concerns about security and dependency.

Contribution

It provides new empirical insights into industry practices and perceptions regarding LLM use in software engineering, bridging the gap between research and real-world application.

Findings

Positive perceptions of LLMs for faster technical resolution

Concerns about security risks and cognitive dependence

Recommendations for responsible and supervised LLM use

Abstract

The rapid advancement of Large Language Models (LLMs) is reshaping software engineering by profoundly influencing coding, documentation, and system maintenance practices. As these tools become deeply embedded in developers' daily workflows, understanding how they are used has become essential. This paper reports an empirical study of LLM adoption in software engineering, based on a survey of 46 industry professionals with diverse educational backgrounds and levels of experience. The results reveal positive perceptions of LLMs, particularly regarding faster resolution of technical questions, improved documentation support, and enhanced source code standardization. However, respondents also expressed concerns about cognitive dependence, security risks, and the potential erosion of technical autonomy. These findings underscore the need for critical and supervised use of LLM-based tools. By grounding the discussion in empirical evidence from industry practice, this study bridges the gap between academic discourse and real-world software development. The results provide actionable insights for developers and researchers seeking to adopt and evolve LLM-based technologies in a more effective, responsible, and secure manner, while also motivating future research on their cognitive, ethical, and organizational implications.