Thursday, April 16th, 14-15:30
Tech Debt is spiraling out of control. A recent CAST report estimated that Tech Debt worldwide has amounted to 61 billion workdays. Gartner, in its “Predicts 2026” report, emphasizes the increasing risk AI agents which assist software engineering tasks pose to systems; that report goes as bold as predicting that “a new remediation market will emerge for specialized tools and consulting services that can audit, identify, and refactor the “AI-generated technical debt” that companies accumulate.” We are approaching a situation similar to where technical debt research began in 2010—marked by uncertainty, confusion, and a lack of clear direction for managing the growing Tech Debt facing developers and organizations, alongside an increasing number of tools promising efficiency and quality that overwhelm software engineers. Only this time driven by AI.
This discussion panel will examine how AI acts as a double-edged sword for Tech Debt: on the one hand AI can potentially provide the tools to automatically identify, measure, prioritize and repay Tech Debt; on the other hand, there is existing evidence that AI is causing the incurrence of massive amounts of Tech Debt through code generation and agentic development. Even if AI tools can provide locally optimized solutions, they often compromise overall design principles and system-wide quality attributes. What will it take to reap the benefits of Generative AI in Tech Debt management while mitigating the risk of making systems even more complex and unsustainable? What are the research directions to prioritize? What does industry really need to solve this problem?
Chairs: Paris Avgeriou and Ipek Ozkaya