Documents

Actions Registration Registration Fees Deadlines and Policies Submit Abstract Guidelines Preparing your Presentation Templates Glossary AI Tools Author's Login Reviewer's Login Ethics of Review Information Conference Details Important Dates Technical Program Social Event Call for Papers Program Committee Event Chairs Keynote Lectures Best Paper Awards Satellite Events Workshops Special Sessions Tutorials Demos Panels Travel and Accommodation Conference Venue About the Region Reaching the City Visa Information Hotel Reservation Partners Academic Partners Industrial Partners Institutional Partners Media Partners Partner Events Publication Partners Previous Conferences Websites Abstracts Invited Speakers Awards Books Published

Keynote Lectures

From Software Engineering Education to Impactful Industry Research: A Blueprint for Impactful Collaboration
Andrea Capiluppi, University of Groningen, Netherlands

Human-Robot Teaming (HRT)
Filippo Sanfilippo, University of Agder (UiA), Norway

Language-Based Software Testing
Andreas Zeller, CISPA Helmholtz Center for Information Security, Germany

From Software Engineering Education to Impactful Industry Research: A Blueprint for Impactful Collaboration

Andrea Capiluppi
University of Groningen, Netherlands

Short Bio
Andrea Capiluppi is an Associate Professor of Software Engineering at the University of Groningen. His earlier research explored empirical software engineering, open-source software ecosystems, and the application of AI and machine learning to software development and collaboration. He currently leads the “Modernisation and Maintenance of Legacy Software” research group, with research interests spanning software evolution, software architecture, maintainability, migration, technical debt, and the long-term sustainability of complex software systems, while maintaining strong collaborations with industry through applied and project-based research.

Abstract
Imagine an academic ecosystem where every project solves real industry problems, every student gains hands-on experience, and every research insight shapes the future of software engineering. This is the power of Industry-as-a-Lab—a model that transforms education into an engine for innovation. At the University of Groningen, the UnICo framework has turned this vision into reality. Over six years, it has connected hundreds of students with over hundreds of industry partners, from local startups to global leaders like ASML. By embedding real-world challenges into academic projects, we’ve created a sustainable collaboration model that delivers concrete results for industry while advancing research and education. This keynote will explore how the four-phase framework (Planning, Preparation, Execution, Follow-up) and root cause analysis ensure long-term success, and how this approach can be replicated to bridge the gap between academia and industry, anywhere.

Human-Robot Teaming (HRT)

Filippo Sanfilippo
University of Agder (UiA), Norway

Short Bio
Filippo Sanfilippo holds a PhD in Engineering Cybernetics from the Norwegian University of Science and Technology (NTNU), Norway, with a focus on intelligent control approaches for robotic manipulators. His research focus on Human-Robot Teaming (HRT), which includes robotics, wearables, human-robot teaming, artificial intelligence, and control theory. He is currently appointed as a Professor at the Faculty of Engineering and Science, University of Agder (UiA), Grimstad, Norway. He carries a vast experience in participating in European research programs and various national projects from the Research Council of Norway (RCN). He is an IEEE Senior Member. He is the former Chair of the IEEE Norway Section. He is the Chair of the IEEE Robotics and Automation, Control Systems and Intelligent Transportation Systems Joint Chapter. He is the Chair of the Norway Section Life Members Affinity Group. He is currently a member of the IEEE Region 8 Chapter Coordination Committee, of the Conference Coordination Committee, of the IEEE Public Visibility Committee, of the IEEE R8 Awards and Recognitions Committee, and of the Professional and Educational Activities Committee. He is also the former Treasurer of the Norsk Forening for Kunstig Intelligens (NAIS), the Norwegian Association for Artificial Intelligence. He has authored and co-authored several technical papers in various journals and conferences. He is a reviewer for several international conferences and journals.

Abstract
Human-robot interaction (HRI) is the study of how humans and robots interact, as well as how to develop robots that can adapt to human behavior. Human-robot cooperation (HRC) expands on this by creating new approaches and technologies that allow robots to collaborate with people in shared environments. The field of human-robot teaming (HRT) goes one step further, by studying how to create teams of humans and robots that can work together effectively and efficiently to achieve common goals. In this talk, an overview of the possible real-life applications for HRT will be presented.

Language-Based Software Testing

Andreas Zeller
CISPA Helmholtz Center for Information Security, Germany
https://andreas-zeller.info

Short Bio
Andreas Zeller is faculty at the CISPA Helmholtz Center for Information Security and professor for Software Engineering at Saarland University. His research on testing and analyzing software has proven highly influential. Zeller is an ACM Fellow, an IEEE Fellow, an IFIP Fellow, holds an ACM SIGSOFT Outstanding Research Award and an IEEE Harlan D. Mills Award.

Abstract
Random test input generators (fuzzers) have become the prime detectors of vulnerabilities in software. While generic fuzzers easily adapt to arbitrary programs under test, they offer very little possibilities to control or shape the generated inputs. In this talk, I present FANDANGO, a novel language-based fuzzer that combines grammars with predicates over input elements to produce inputs that satisfy all the given predicates. Examples of what such predicates can express include: * input format constraints ("The `length` field should be equal to the length of the payload") * checksums ("The `signature` field should be a SHA-512 hash of the `document`") * statistical distributions ("Across all inputs, the `voltage` field must follow a Gaussian distribution, but never exceed 20 mV") * data collections ("The `credit-card-number` field should come from the Python faker library") and more – actually, any property that can be expressed in a Python expression. The whole becomes even the more interesting as grammars, protocol features, and data collections can be queried from Large Language Models. In our experiments, FANDANGO efficiently solved complex file formats and satisfied demanding predicates, up to full-fledged programming languages as test inputs for compilers. This opens the door towards personalized fuzzing, where testers can make use of their own knowledge and LLM knowledge to very effectively fuzz systems. Includes live demos! Fandango is available at https://fandango-fuzzer.github.io/