Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study

TL;DR

This study empirically examines how German software engineers adopt and utilize generative AI tools, highlighting organizational, experience-related, and regulatory factors influencing effective use and productivity impacts.

Contribution

It provides the first systematic empirical analysis of GenAI adoption in the German software engineering industry, revealing key influencing factors and barriers.

Findings

Experience level affects perceived benefits of GenAI.

Organizational size influences tool selection and usage intensity.

Limited awareness of project context is a major barrier.

Abstract

Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers. While adoption rates in the industry are rising, the underlying factors influencing the effective use of these tools, including the depth of interaction, organizational constraints, and experience-related considerations, have not been thoroughly investigated. This issue is particularly relevant in environments with stringent regulatory requirements, such as Germany, where practitioners must address the GDPR and the EU AI Act while balancing productivity gains with intellectual property considerations. Despite the significant impact of GenAI on software engineering, to the best of our knowledge, no empirical study has systematically examined the adoption dynamics of GenAI tools within the German context. To address this gap, we present a comprehensive mixed-methods study on GenAI adoption among German software engineers. Specifically, we conducted 18 exploratory interviews with practitioners, followed by a developer survey with 109 participants. We analyze patterns of tool adoption, prompting strategies, and organizational factors that influence effectiveness. Our results indicate that experience level moderates the perceived benefits of GenAI tools, and productivity gains are not evenly distributed among developers. Further, organizational size affects both tool selection and the intensity of tool use. Limited awareness of the project context is identified as the most significant barrier. We summarize a set of actionable implications for developers, organizations, and tool vendors seeking to advance artificial intelligence (AI) assisted software development.