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

2026-01-23

AI Summary

This study addresses the lack of systematic empirical research on the adoption of generative AI (GenAI) tools in German software engineering under the stringent regulatory landscape of the European Union, particularly the GDPR and the AI Act. Employing a mixed-methods approach, the project combines 18 in-depth interviews with 109 developer surveys to uncover key constraints shaping GenAI uptake in this regulated context. Findings reveal that developer experience and organizational size significantly moderate perceived tool benefits, while “insufficient project context awareness” emerges as a central barrier. Moreover, productivity gains from GenAI exhibit substantial individual variation. The results offer actionable insights for developers, organizations, and tool vendors to facilitate the effective and compliant integration of AI-assisted development practices within regulated environments.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: AI for Accessibility
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Generative Artificial Intelligence
• Software Engineering
• Technology Adoption
• Regulatory Compliance
• Empirical Study

Innovation

• Generative AI
• Software Engineering
• Empirical Study
• AI Adoption
• Regulatory Compliance