Associate Professor · Aalborg University · Copenhagen
Associate Professor of Software Engineering at Aalborg University. My research examines how people, processes, and education interact within software organisations and translates those findings into guidance that practitioners and technology leaders can act on.
“Most decisions about AI adoption, engineering team design, and agile transformation are made without a scientific approach. That gap is what this work addresses.”
About
■ Ph.D., University of Bologna
■ Tenured Associate Professor, Aalborg University
I am a tenured Associate Professor of Software Engineering at Aalborg University in Copenhagen. My research uses empirical methods, including large-scale surveys, longitudinal studies, and structural equation modelling, to establish credible, reproducible findings about how software teams, tools, and organisations function.
The question that organises this work is practical: what does the evidence say, and what should an engineering leader or team do with it? Over fifty peer-reviewed publications later, all open access and deposited in Zenodo for replication, I translate those findings into plain language, with the source attached.
That translation is the purpose of this site and of the Software Insights newsletter.
Research
Empirical studies on the questions that matter most to engineering teams and technology leaders today.
01
What makes AI tools actually stick in engineering teams — and what the evidence shows when the hype settles.
AI changes what engineers build, not who they are. This research examines how developers adopt AI tools, how teams collaborate under pressure, and what well-being, cognition, and trust look like when the tools get smarter.
02
What empirical studies say about agile transformation, remote work, and the real effects of diversity on team performance.
Software processes have always been coordination problems. Agile solved the human side; agentic systems are now rewriting the rules at every layer. This research follows that thread from Scrum team effectiveness to AI-orchestrated pipelines.
03
How software engineers actually learn — and what the evidence says about the formats, frameworks, and communities that make it work.
Generative AI does not just change how we write code. It redefines what it means to be a software engineer. This research builds frameworks for teaching, learning, and rethinking the curriculum at every level.
Methods, Scientific Practice Base
Every pillar of this work rests on a shared commitment to methodological rigour and participatory practice. This means designing empirical studies that hold, from PLS-SEM modelling to recruiting software engineers at scale, from shared standards for replication to guidelines for studying AI systems responsibly.
Software Insights, Newsletter
Each issue takes one peer-reviewed study and explains what it found, why it matters, and what an engineering leader can do with it. No opinion without evidence. No finding without a source.
March 2026
Your Agents Pass Every Test. Your System Can Still Fail.
March 2026
Cutting the Junior Pipeline Before the Automation Arrives
March 2026
AI Can Solve Your Benchmark. Can It Fix Your Codebase?
Initiatives
Four channels through which this research connects to organisations, practitioners, and the broader research community.
Evidence-Based Advisory
If your organisation is working through AI tool adoption, engineering team design, or process change, the research literature has findings that are directly relevant. I bridge the gap between published evidence and the specific decision in front of you.
Newsletter
A newsletter that reads the empirical software engineering literature so you do not have to. Each issue takes one peer-reviewed finding and makes it legible for engineering leaders and senior practitioners who want evidence, not opinion.
Annual, Invitation Only
A working forum funded by the Alfred P. Sloan and Carlsberg Foundations. Produces peer-reviewed publications, workstreams, and the Copenhagen Manifesto on human-centred AI in software engineering.
Sloan Foundation
Carlsberg Foundation
Bi-Annual, Copenhagen
A bi-annual gathering connecting academic researchers with technology decision-makers. Participants arrive with a problem; they leave with evidence-based perspectives and new collaborators.
Research + Industry
Copenhagen
Teaching
Teaching is where the Education pillar of my research programme becomes practice. Courses at Aalborg University in Software Engineering, Human-Centered AI, and Structural Equation Modelling are designed around evidence about how practitioners develop expertise, not around received curricula.
Evidence-based project work: empirical methods, Agile processes, AI integration. Aalborg University PBL model.
Designing AI systems for human contexts: HACAF framework, participatory design, responsibility. Direct expression of the Education pillar.
PLS-SEM for software engineering research: hands-on with SmartPLS. Connected to the Methods page.
Doctoral students working at the intersection of empirical software engineering and any of the three research pillars. Discuss fit
How I Can Help You
If your organisation is working through AI tool adoption, engineering team design, or process change, the research literature has findings that are directly relevant. Use the form below to describe your context. I will respond within two working days.