Klaus Haeuptle

This session follows our journey at SAP from developer frustration to establishing the large scale refactoring capability. Well share how we moved from grassroots discovery and community workshops to building a robust business case for automation. Learn how we leveraged an InnerSource model to bridge the gap between central technology teams and product units, effectively “paving the road” for seamless upgrades, migrations, and recurring code changes.

Join a panel of trailblazing engineering leaders who are shaping how the industry thinks about scaling software development. From navigating technical debt to responsibly integrating generative AI, this conversation will cover the hard-won lessons and emerging strategies for managing massive codebases.

We’ll dive into:

• Impact of developer time spent on code refactoring.
• The influence of generative AI code creation on automated code refactoring.
• Techniques for ensuring genAI authored code is high quality, maintainable, and secure—and proving it meets standards.
• Organizational challenges of managing large-scale code changes, including PR overload, developer trust, and risk mitigation.
• Measuring the impact of technical debt across large software organizations and driving leadership action.
• Risk assessment and mitigation strategies when rolling out changes that affect thousands of developers.
• The role of auto-refactoring in developer portals and real-world use cases for integration.
• Challenges and lessons learned from using generative AI for testing and dead code removal at scale.

Come with questions—this panel is built to spark insight and honest discussion.