Dr. Antonio Martini
From Whispers to Strategy: the Benefits of Visualizing Technical Debt Management
Abstract: Technical debt is pervasive, costly, and too often managed by gut feelings. Effective management requires visibility. However, existing technical debt visualization tools are too code‑centric and fragmented: they are tied to specific languages, rely on static metrics that miss context, and seldom reshape how organizations prioritize or act. Practitioners wish for more and better Technical Debt visualization. This keynote will move beyond metrics like the isolated complexity scores and code duplications to cover more holistic project- and organization-wide visualizations that give a stronger decision support to manage Technical Debt.
The keynote draws on more than a decade of research in collaboration led by Prof. Martini with 20+ international companies based in North Europe such as Visma, Akva Group, Knowit, Vy and Siemens. It will showcase visualizations from tools like TD Pulse, DebtQuest and novel AI-powered solutions integrating multiple data sources in meaningful insights, that reveal gaps in organisational scaffolding and practices, inter- and cross-team misalignment, role friction, issue resolution patterns, and their ties to KPIs. The results of our latest research, an ongoing three-year Norwegian innovation project, shows that these visualizations have a strong learning effect and consistently help assessing and improving the management of technical debt in various organizations.
| Antonio Martini's Bio: Antonio Martini is a Professor at the University of Oslo, whose research lies at the intersection of Artificial Intelligence and Software Engineering. His recent work focuses on leveraging AI to advance software engineering practices—particularly in managing Technical Debt, improving software architecture, and enhancing engineering processes for AI systems. His broader research interests include technical leadership, agile software development, and software quality management. He is passionate about collaborating with software companies to bridge research and practice, striving to co-create innovative solutions that improve the state of the art and the state of practice in the software industry. His experience spans a wide range of contexts, including large embedded software organizations, small web-based companies, B2B enterprises, and startups. His expertise covers everything from technical programming and software architecture to agile methodologies and software business strategy. Dr. Martini has collaborated with numerous leading companies such as Ericsson, Volvo, Saab, Axis, Grundfos, Siemens, Bosch, Jeppesen, Visma, Knowit, and AKVA Group, among others. He has also led industrial projects on managing and visualizing technical debt, including initiatives with Ericsson EPG and Volvo IT. |
Dr. David Lo
AI and Technical Debt: Friend or Foe?
Abstract: As AI becomes an increasingly pervasive force in modern software development, it is reshaping how systems are created, reviewed, and evolved across the software lifecycle. At the same time, technical debt remains an inescapable reality, weighing on maintenance effort, change velocity, and system reliability. This keynote focuses on the intersection where these two forces meet and the questions it raises for both researchers and practitioners: What happens when AI and technical debt intersect? Will they be friends – or will they turn into foes? This keynote will explore these questions, highlighting empirical findings, existing challenges, and future directions at the exciting intersection of technical debt and AI4SE.
| David Lo's Bio: David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. Championing the field of AI for Software Engineering (AI4SE) since the mid-2000s, he has demonstrated how AI — encompassing data mining, machine learning, information retrieval, natural language processing, and search-based algorithms — can transform software engineering data into actionable insights and automation. Through empirical studies, he has identified practitioners’ pain points, characterized the limitations of AI4SE solutions, and explored practitioners’ acceptance thresholds for AI-powered tools. He regularly contributes to the MSR conference and works on various MSR topics, including how MSR provides insight into the reliability of machine learning (ISSRE’12) and powers automated program repair (SANER’16). His contributions have led to over 20 awards, including two Test-of-Time awards and eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards, and his work has garnered over 38,000 citations. An ACM Fellow, IEEE Fellow, ASE Fellow, and National Research Foundation Investigator (Senior Fellow), Lo has also served as the GC of MSR’22 and ASE’16, and as a PC Co-Chair for ASE’20, FSE’24, and ICSE’25. For more information, please visit: http://www.mysmu.edu/faculty/davidlo/. |
Justine Gehring
Three Flavors of Technical Debt in Practice
Abstract: We often frame technical debt as aging code, libraries, and frameworks, but that is only one part of the picture. In this keynote, I argue for three practical categories of technical debt: legacy code, missing automation, and infrastructure cost. This includes teams who “do not have time” to build CI/CD but somehow find time to SSH into machines for every release, and organizations that never quite have time to modernize infrastructure and therefore stay locked into expensive platforms and licenses.
A recurring challenge across all three is ROI: it is hard to calculate, which makes technical debt easy to tolerate far longer than it should be. Drawing on industry examples, I discuss how teams are paying down these debts, outsourcing them, automating them, or choosing to live with them for now. I also outline concrete approaches to remediation, and yes, unsurprisingly, some of the answers involve AI.
| Justine Gering's Bio: Justine Gehring, a Mila Alumni and head of AI at Gologic, works across deterministic and non-deterministic approaches to remediate tech debt in all its forms, including legacy software, CI/CD gaps, infrastructure cost, IaC drift, observability, and security posture. She partners with medium and large enterprises, often in financial and manufacturing sectors, to apply AI to real operational bottlenecks faced by their IT teams, with a clear focus on productivity. For her, the right solution pairs AI agents with precise tools and guardrails so models act with traceability, reproducibility, and policy compliance you can trust. Because credible remediation requires proof, and she is still a researcher at heart, she also builds the evaluations: two datasets designed to test AI or non-AI solutions, including one accepted at a NeurIPS workshop. |
Dr. Margaret-Anne Storey
From Technical Debt to Cognitive Debt
Abstract: As generative AI is increasingly adopted by software developers under pressure to accelerate how they author, maintain, and evolve software systems, concerns about technical debt become more, not less, pressing. Traditional forms of technical debt, such as complex dependencies, brittle architectures, missing tests, and inadequate documentation, reside in software artifacts and are, in principle, observable and refactorable by humans or automated agents over time.
However, this artifact-centric view overlooks a growing source of long-term cost: the human cognitive burden introduced by AI-assisted development. In this talk, I introduce cognitive debt, the accumulation of future mental effort required to understand, reason about, and collaborate around a software system. While generative AI can reduce local cognitive load through summaries, explanations, and code generation, it can simultaneously increase longer-term cognitive debt through shallow system-level understanding, fragmented knowledge, lost learning opportunities, and reliance on ephemeral prompts.
This reframing shifts the focus of technical debt from what is expensive to fix in the codebase to what is cognitively sustainable for the people and teams responsible for evolving, overseeing, or intervening in the system over time. If a system is technically refactorable but no longer cognitively sustainable, have we truly managed its technical debt?
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Margaret-Anne Storey's Bio: Margaret-Anne Storey is a Professor of Computer Science and a Canada Research Chair in Human and Social Aspects of Software Engineering. Together with her students and collaborators, she seeks to understand how software tools, communication media, data visualizations, and social theories can be leveraged to improve how software engineers and knowledge workers explore, understand, analyze and share complex information and knowledge. She has published widely on these topics and collaborates extensively with high-tech companies and non-profit organizations to ensure real-world applicability of her research contributions and tools. Dr. Storey currently advises companies that include Microsoft and DX on strategies to understand and improve developer productivity and developer experience. For more information, please visit: https://margaretstorey.com/. |