Daniel Russo, Ph.D.

A scientific mindset for software engineering in the AI era.

Associate Professor of Software Engineering at Aalborg University in Copenhagen. My research asks what the evidence actually says about how software gets built: how AI is changing the work, how people and teams shape delivery outcomes, and how the next generation of engineers should be taught.

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Daniel Russo, Ph.D., Associate Professor of Software Engineering
60+
Publications
3,000+
Citations
€3M+
Funded research
Ph.D.
Bologna
Tenured

Research rigour, practitioner relevance

Ph.D., University of Bologna Tenured Associate Professor, Aalborg University Member, Danish Young Academy

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? More than sixty peer-reviewed publications later, all open access, I translate those findings into plain language, with the source attached.

That translation is the purpose of this site. Research maps the programme across its three pillars, Publications holds the complete open-access archive, and Teaching shows how the evidence shapes the classroom. For organisations, Advisory brings the findings to specific decisions, and the Software Insights newsletter examines one peer-reviewed study each week.

Flagship research initiative Active

AI4SE1DK: Human-Centred AI for Software Engineering

Grand Solutions
Innovation Fund Denmark

A 48-month national programme establishing Denmark as a global reference for trustworthy, human-centred adoption of AI in software engineering, closing the current adoption gap while strengthening the competitiveness of Danish software organisations and their workforce.

View the project Project website →
30.4M
DKK total budget
14
Partners
10K+
Practitioners targeted
2029
Project end
SDU, AAU, KU, AU, ITU, DTU, Alexandra Institute, Ericsson, Umbraco, Systematic, SIG, DCR, Unik, ESS
AI in Software Engineering

Workflow compatibility predicts adoption more than perceived usefulness.

Russo (2024), ACM Transactions on Software Engineering and Methodology. A survey of 183 software engineers, analysed with structural equation modelling.

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FIG 1.1 HACAF structural model (Russo, 2024, ACM TOSEM) Path diagram of the structural model of the Human-AI Collaboration and Adaptation Framework (HACAF), estimated with PLS-SEM on 183 software engineers. Perceptions about the Technology predicts Compatibility Factors (0.766, p < 0.001), and Compatibility Factors predicts Intention to Use (0.536, p < 0.001); this is the highlighted path. Perceptions about the Technology also predicts Social Factors (0.405, p < 0.001), and Personal and Environmental Factors predicts Perceptions about the Technology (0.313, p < 0.001). The direct paths to Intention to Use from Perceptions about the Technology (0.155), Social Factors (0.087) and Personal and Environmental Factors (minus 0.004) are not significant. Variance explained (R squared): Compatibility Factors 0.587, Intention to Use 0.489, Social Factors 0.164, Perceptions about the Technology 0.098. 0.313*** −0.004 n.s. 0.155 n.s. 0.766*** 0.536*** 0.405*** 0.087 n.s. Personal and Environmental Factors Perceptions about the Technology R² = 0.098 Intention to Use R² = 0.489 Compatibility Factors R² = 0.587 Social Factors R² = 0.164 FIG 1.1 HACAF structural model. PLS-SEM, standardised path coefficients with R² for endogenous constructs; bootstrapping with 5,000 subsamples; n = 183 software engineers. *** p < 0.001; n.s. p > 0.05, dashed. Highlighted: the only significant direct predictor of Intention to Use. Source: Russo (2024), ACM TOSEM 33(5), Fig. 2, Tables 13, 14. doi:10.1145/3652154. CC BY 4.0, redrawn.
FIG 1.1 HACAF structural model, core paths (Russo, 2024, ACM TOSEM) Path diagram of the core paths of the Human-AI Collaboration and Adaptation Framework (HACAF), estimated with PLS-SEM on 183 software engineers. Perceptions about the Technology predicts Compatibility Factors (0.766, p < 0.001), and Compatibility Factors predicts Intention to Use (0.536, p < 0.001); this is the highlighted path. The direct path from Perceptions about the Technology to Intention to Use (0.155) is not significant. Variance explained (R squared): Compatibility Factors 0.587, Intention to Use 0.489. Four further paths of the full model are listed as text below the diagram. 0.155 n.s. 0.766*** 0.536*** Perceptions about the Technology (PT) Compatibility Factors (CF) R² = 0.587 Intention to Use (IU) R² = 0.489 Also in the full model, not drawn here: PEF PT 0.313*** PT SF 0.405*** PEF IU −0.004 n.s. SF IU 0.087 n.s. PEF: Personal and Environmental Factors. SF: Social Factors. R² from the full model. FIG 1.1 HACAF structural model, core paths. PLS-SEM, standardised path coefficients; n = 183 software engineers. *** p < 0.001; n.s. p > 0.05, dashed. Highlighted: the only significant direct predictor of Intention to Use. Source: Russo (2024), ACM TOSEM 33(5), Fig. 2, Tables 13, 14. doi:10.1145/3652154. CC BY 4.0, redrawn.
Three research pillars
01

AI in Software Engineering

AI is changing software development. Whether it is changing it in the ways the conversation assumes is an empirical question.
02

People, Teams & Organisations

The most consequential variables in delivery outcomes are not technical. They are organisational, interpersonal, and psychological.
03

Education & the Next Engineer

How software engineers learn has received far less empirical attention than how they work. This research closes that gap.

The full research programme

Good research begins before the question.
Daniel Russo

Sixty-plus papers, all open access.

Every peer-reviewed paper, grouped by year and filterable by topic and venue. Each entry carries a plain-language summary of what the study found and a citation ready to copy.

From Challenge to Change: Design Principles for AI TransformationsThe Journal of Systems & SoftwareJournal
2027Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering PurposesInformation and Software TechnologyJournal
2026Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native SoftwarearXiv preprintPreprint
2026

Browse the publication archive

Software Insights, latest
What the productivity question no longer tells youNov 2026 When the agent speaks first, whose judgment is it?Nov 2026 What makes a coding agent worth keeping?Oct 2026

One peer-reviewed study, translated into practice, every week. Free.

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Evidence in the classroom.

I teach bachelor, master, and PhD programmes at Aalborg University across software engineering, artificial intelligence, and research methodology, and supervise theses in each.

Teaching and supervision

Evidence-based advisory

The research is there. Let’s put it to work.

Short diagnostics, embedded organisational audits, and ongoing advisory for engineering leaders.

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