Just like yesteryear, AI is on everyone’s minds in the software industry. From my vantage point at CodeScene, I encounter a wide range of human reactions in discussions with our customers. All feelings are valid, and the Gibson quote “The future is already here, it’s just not very evenly distributed” is in full swing – especially from the perspective of AI agents. Few developers are entirely leaving out LLM assistance these days, but the use of coding agents varies widely. What happens to collaboration, learning, and responsibility as teams shift to hybrid human-AI development?

In this keynote, I argue that behavioral code analysis becomes even more important as AI gets agency. By focusing on social signals such as knowledge distribution, ownership, and coordination, we can mitigate challenges related to provenance, deskilling, and maintainability. I share a personal perspective with elements of industry-academia collaboration, reflections from a tool vendor, research highlights, and new case study results with our partner loveholidays.

Dr. Markus Borg is a senior researcher at the intersection of software engineering and applied artificial intelligence. He is a principal researcher at CodeScene and an adjunct lecturer at Lund University. Markus serves on the editorial board of Empirical Software Engineering and is a department editor for IEEE Software.

My goal is to support the successful engineering of software and data-intensive systems. While my software engineering interests are broad, most of my work relates to machine learning, i.e., my research interests span both software engineering intelligence (AI4SE) and AI engineering (SE4AI).

In AI4SE, I primarily work on maintainability guardrails for coding agents. Before the agentic era, I’ve mostly worked on tapping into the collected wisdom of historical project data to facilitate machine learning for actionable decision support. My most impactful contributions have been in defect management, for example, bug assignment and change impact analysis. Core ideas are currently operationalized in internal tools at Ericsson.

In SE4AI, I investigate quality assurance of systems that embed machine learning components. I am particularly interested in development mandated by automotive safety standards and the EU AI Act. Our research studies involve requirements engineering, MLOps pipelines, software testing in automotive simulators, and our open-source demonstrator SMIRK.