James R. Cordy is a Professor in the School of Computing at Queen's University, Faculty of Engineering and Applied Science, Kingston, Canada. He is a leading researcher in software engineering, with a focus on source code analysis, software clone detection, model-driven engineering, and program transformation. He has been actively publishing since 1977, with a sustained record of contributions in top-tier venues such as ICSE, MoDELS, and WCRE. His research interests include software clone detection, model transformation, Simulink models, source transformation, software maintenance, and grammatical inference. He has developed and contributed to influential tools such as TXL and NiCad, and his work often involves empirical studies and tool evaluation in real-world software systems. The most recent articles highlight trends in model transformation, clone detection, verification of state machines, and migration of legacy systems. His work increasingly integrates formal methods and empirical validation, particularly in automotive and safety-critical domains. He has also explored applications in healthcare software, such as artificial pancreas systems. Most Influential Paper Award, SCAM 2001 (awarded in 2019) He has advised numerous students, including Manar H. Alalfi, Matthew Stephan, and Chanchal K. Roy, who have co-authored multiple publications with him. His research is often collaborative, involving teams from Queen's University and other institutions. He has also contributed to workshops and special issues, demonstrating leadership in the software engineering community.
Christian Dietrich is a Professor for Reliable Distributed Systems at Technical University of Braunschweig since 2024, where he heads the Reliable System Software research group within the Institute of Operating Systems and Computer Networks, part of the Faculty of Electrical Engineering, Information Technology, and Physics. He previously held positions at Leibniz Universität Hannover where he completed his distinguished doctoral research. Professor Dietrich's research focuses on operating systems, distributed systems, and reliable systems with particular expertise in memory management, real-time systems, and embedded systems. His work spans both theoretical foundations and practical implementations, with significant contributions to system software reliability and efficiency. His research interests include operating system design, memory management techniques, real-time computing, embedded systems, fault tolerance, and compiler optimizations for system software. Dietrich's recent publications demonstrate a strong focus on memory management innovations (particularly for persistent memory), virtualization techniques, and reliability mechanisms for distributed systems. His work frequently bridges the gap between theoretical computer science and practical system implementation, with many contributions finding their way into production systems. The research shows increasing emphasis on hardware-software co-design and adapting operating systems to modern hardware capabilities. His scientific achievements have been recognized with numerous prestigious awards: USENIX ATC Distinguished Artifact Award (2023) for LLFree USENIX ATC Best Paper Award (2017) for cHash RTAS Best Paper Award (2015) for dOSEK Multiple Outstanding Paper Awards at ECRTS, OSPERT, and ISORC Wissenschaftspreis Hannover (2020, awarded in 2024) Professor Dietrich has secured significant research funding including multiple DFG projects (DI 2840/1-1, DI 2840/2-1) and has been involved in collaborative projects with industry partners. His teaching portfolio includes advanced courses on operating systems, programming languages, and compilers, for which he received teaching excellence awards. He leads an active research group focused on reliable system software with ongoing projects including ParPerOS (Parallel Persistency OS) and ATLAS (Adaptable Thread-Level Address Spaces). The Reliable System Software group maintains strong connections with both academic and industrial partners, contributing to open-source projects and collaborating on cutting-edge research in system software reliability. The group's work has practical impact, with some findings leading to more than 100 accepted patches in the Linux mainline kernel.
Professor Daniel Lohmann leads the Systems Research and Architecture (SRA) group at Leibniz Universität Hannover since 2017, following his role as Associate Professor (Privatdozent) at Friedrich-Alexander-Universität Erlangen-Nürnberg (2009–2016). His work focuses on configurable system software, emphasizing embedded systems, real-time systems, and dependable architectures. Key projects include danceOS (dependability in embedded OS), Sloth (minimal-effort kernels), and AspectC++ (aspect-oriented C++ extension). His research is funded by DFG and addresses challenges in variability management, fault tolerance, and real-time scheduling. Research interests span operating systems design, software product lines, generative programming, and aspect-oriented development. Notable contributions include the CiAO OS family and the Sloth RTOS, leveraging aspect-oriented techniques for flexibility and efficiency. His work on configuration analysis (e.g., static variability in Linux) and fault mitigation (e.g., AN-Codes for soft errors) highlights his expertise in system reliability and optimization. Publications emphasize real-time systems, embedded software, and software engineering, with awards including the Best Paper Award at SPLC 2010 and an Outstanding Paper at RTAS 2017. He supervises theses on system software tailoring, fault tolerance, and industrial software ecosystems. His groups collaborate on invasive computing (SFB/TRR 89) and hardware-aware OS design.
Prof. Dr.-Ing. Thomas Leich holds the Volkswagen Financial Services Endowed Professorship for Business Informatics, specifically Requirements Engineering, at Harz University of Applied Sciences. He is affiliated with the Faculty of Automation and Information, focusing on software variability and configurable systems. University: Harz University of Applied Sciences School: Faculty of Automation and Information Academic Rank: Professor Research Interests span software product line engineering, IT security, Industry 4.0, and feature-oriented programming. His work bridges academic innovation with industrial applications, particularly in secure and scalable systems for automotive and enterprise contexts. Recent Publications emphasize security in configurable systems, empirical software engineering studies, and automotive platform management. Articles often involve collaborations with teams at Otto von Guericke University and international institutions. Scientific Awards include the Hugo Junkers Research Award (2013), the Wissenschaftspreis der Wernigeröder Stadtwerkestiftung (2017), and the 2012 Faculty Research Award from the University of Magdeburg. Supervision of over 20 theses since 2008, spanning topics in ERP security, program comprehension, and embedded systems. He leads the EXPLANT project, funded by DFG, and contributes to FeatureIDE , an extensible framework for feature-oriented development.
Jean-Marc Jézéquel is a prominent professor and researcher at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires) in Rennes, France, a joint research unit of INRIA, CNRS, and the University of Rennes. His extensive publication record spanning over two decades demonstrates his significant contributions to software engineering, particularly in model-driven engineering and software product lines. His research expertise encompasses several critical areas in contemporary software engineering. Jézéquel has pioneered work on managing uncertainty in model-based development, developing frameworks like DataTime for temporal reasoning in models. He has made substantial contributions to deep variability management across multiple layers of software systems, investigating how compile-time and run-time configuration options interact. His recent work increasingly integrates machine learning techniques with traditional software engineering approaches, creating innovative hybrid methodologies for software development and analysis. Jézéquel's publication trends reveal a clear evolution in his research focus, moving from foundational model-driven engineering concepts toward addressing uncertainty, incorporating temporal aspects into models, and leveraging machine learning for software analysis and development. His work spans both theoretical foundations and practical industrial applications, with notable collaborations including projects with Airbus on specialized software product line engineering. The interdisciplinary nature of his research connects software engineering with artificial intelligence, data science, and systems engineering. His significant contributions to the field include foundational work on model transformation, software language engineering, and variability management. Jézéquel has helped advance model-driven engineering from what he describes as 'craft' to a more systematic engineering discipline, as evidenced in his publications spanning from early 2000s to the present. As an academic leader, Jézéquel has supervised numerous PhD students and maintained extensive international collaborations. His work demonstrates a consistent commitment to bridging the gap between academic research and industrial software development practices, with applications ranging from aerospace systems to urban transportation solutions.
Andreas Ziegler is a Professor at the Department of Computer Science (INF) of Friedrich-Alexander University Erlangen-Nürnberg. His research focuses on system software optimization , particularly in Linux kernel configurability and binary tailoring . He leads the Chair of Computer Science 4 (System Software) and develops open-source tools for minimizing software stacks while preserving functionality. University: Friedrich-Alexander University Erlangen-Nürnberg School: College of Engineering Department: Department of Computer Science Rank: Professor Research Interests: Ziegler investigates methods to automate software stack tailoring. His work spans: Configuration Interface Utilization (e.g., Linux kernel modules) Binary-Level Code Removal (ELF file manipulation without source access) Maintenance Impact Quantification (AST hashing for change detection) Publication Trends: Recent works emphasize attack surface reduction and scalable configuration testing , while earlier studies focus on feature modeling and compilation redundancy . Tools developed include GitHub-hosted open-source solutions . Advising: Supervised multiple Master’s theses on topics like header analysis for dead code detection and dynamic variability management in Linux systems.
Tillmann Rendel is a Research Fellow at the University of Tübingen , affiliated with the Department of Programming Languages and Software Technology . He contributes to projects involving DSLs , language design , and program transformations . Education: M.Sc. in Computer Science. Research interests center on programming languages , focusing on modular systems , functional programming , and domain-specific language implementation . His work explores invertible syntax descriptions , copattern matching , and variability-aware testing . Publications highlight advancements in parsing , DSL design , and language composition , with a strong emphasis on formal semantics , compiler design , and interactive development tools . Scientific Awards: Most Influential Paper Award at GPCE 2018 for 'Polymorphic Embedding of DSLs' (2008 paper co-authored with Hofer, Ostermann, and Moors). Collaborations include key contributions to the Uroboro project , participation in Dagstuhl Seminars , and presentations at major conferences like OOPSLA , ICFP , and DSLDI .
Lola Burgueño is an Associate Professor at the University of Malaga (UMA), Spain, where she is a member of the Atenea Research Group and part of the Institute of Technology and Software Engineering (ITIS). She completed her PhD with honors in April 2016 from the University of Málaga, following a master's degree in Software Engineering and Artificial Intelligence (2012) and a Computer Science and Engineering degree (2011). Her academic journey includes postdoctoral positions at the Open University of Catalonia (Barcelona, Spain; 2018-2022) and CEA List (Paris, France; 2018-2020), along with visiting researcher appointments at the University of Alabama (USA; 2014), Vanderbilt University (USA; 2016), and Université de Montréal (Canada; 2022). Dr. Burgueño's research spans several interconnected domains: Software Engineering and Model-Driven Software Engineering Artificial Intelligence integration in software development processes Uncertainty management during software design phases Model-based software testing methodologies Performance optimization of model transformations Digital twin technologies and Socio-Cyber-Physical Systems Her publication record shows an evolving research trajectory from foundational work in model transformations and non-functional requirements toward increasingly sophisticated integration of AI techniques with traditional software engineering approaches, with recent emphasis on ethical considerations in AI-enhanced software modeling and human-centered aspects of digital twin technologies. Professional service and recognition: PC (co-)chair for ECMFA'21, SLE'22 and JISBD'25 Active Program Committee member for ASE, ICSE, MODELS, SLE, and other premier software engineering conferences Editorial Board member of the Journal on Software and Systems Modeling (SoSyM) Steering Committee member of the ACM SIGPLAN International Conference on Software Language Engineering (SLE) Member of the WG on Diversity & Inclusion of Informatics Europe Dr. Burgueño maintains strong international collaborations across Europe and North America, regularly reviewing for top-tier journals including TSE, JSS, EMSE, and SoSyM, and contributing to the advancement of software engineering research and education globally.
Paul Grünbacher serves as an Associate Professor and Deputy Head of the Institute of Software Systems Engineering at Johannes Kepler University Linz, Austria. With over 150 publications in international peer-reviewed venues, he's established himself as a leading researcher in software engineering, particularly in specialized domains requiring sophisticated modeling approaches. His primary research interests include software product lines , model-based development , software evolution , requirements engineering , and software monitoring . These interconnected fields reflect his focus on creating adaptable, maintainable software systems that can evolve over time while maintaining quality and functionality. His work bridges theoretical foundations with practical applications in complex software environments. Analysis of his recent publications reveals a consistent focus on temporal aspects of software evolution, particularly in product line contexts. His research demonstrates strong emphasis on creating methods to track, analyze, and support software changes across multiple dimensions including spatial distribution and temporal progression. The increasing integration of monitoring techniques with requirements engineering represents an emerging trend in his more recent work. Fellow of Automated Software Engineering (2021) Editorial Board Member, Empirical Software Engineering Journal (Springer, 2018-present) Editorial Board Member, Information and Software Technology Journal (Elsevier, 2015-present) Steering Committee Member, IEEE/ACM International Conference on Automated Software Engineering (2004-present) Grünbacher has served as a program committee member for numerous prestigious conferences including ASE, ICSE, and REFSQ across multiple years. He led the Christian Doppler Laboratory for Monitoring and Evolution of Very-Large-Scale Software Systems from 2013-2021, securing significant research funding and directing a team focused on practical solutions for evolving complex software systems. His editorial roles demonstrate recognition of his expertise in empirical software engineering methodologies. As head of the Christian Doppler Laboratory for Monitoring and Evolution of Very-Large-Scale Software Systems (2013-2021), Grünbacher directed a research team developing innovative approaches for monitoring and evolving complex software systems. His current work through the Institute of Software Systems Engineering continues this focus, with increasing attention to cyber-physical systems and digital process twins.
Valentin Rothberg is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His work focuses on operating systems, software configuration management, and compiler optimization. He is a core contributor to projects like CADOS (Configurability Aware Development of Operating Systems) and VAMOS (Variability Management in Operating Systems). Research interests include Linux kernel customization, dynamic variability in software systems, and compiler-based redundancy detection. He has authored notable publications on AST hashing for build optimization and impact analysis of Linux feature changes. Rothberg has advised multiple theses on topics like kernel configuration analysis and static analysis tools. His open-source contributions include the vgrep tool for enhanced text search and collaboration on container technologies like Podman . He maintains active involvement in FAU's distributed systems research and has presented at conferences such as USENIX and VaMoS.
Dr. Julio Sincero is a former researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems Group) at the University of Erlangen-Nuremberg. His academic work focuses on variability management in system software, configuration analysis, and software product lines, particularly in the context of operating systems like Linux. He holds a PhD from Friedrich-Alexander-Universität Erlangen-Nürnberg (2013). His research explores challenges such as variability bugs, configuration inconsistencies, and non-functional property management in large-scale systems. He contributed to projects like VAMOS (Variability Management in Operating Systems) and the CiAO framework for embedded systems. Teaching activities include courses on operating systems and related technical seminars. Notable publications span static analysis of system software variability, configuration coverage techniques, and Linux kernel feature management. He has collaborated with institutions worldwide, addressing topics like many-core system reengineering and software product line adaptability. Dr. Sincero's work emphasizes practical solutions for scalability and maintainability in complex system software, with a focus on bridging theoretical research and real-world implementation challenges.
Professor Heike Trautmann is a distinguished academic at Paderborn University, where she serves as Professor of Machine Learning and Optimisation in the Department of Computer Science within the Faculty of Computer Science, Electrical Engineering and Mathematics. Since April 1, 2025, she also holds the position of Vice President for International Relations at the university. Her academic career spans prestigious institutions including the University of Münster, where she was Professor of Data Science: Statistics and Optimization from 2013 to 2023, and the University of Twente, where she serves as Guest Professor of Data Science until February 2026. Dr. Trautmann's educational background includes: University Studies in Statistics (Diploma), TU Dortmund, Germany (1997-2000) University Studies in Economic Mathematics (First Diploma), TU Dortmund, Germany (1996-1998) PhD student at Graduate School of Production Engineering and Logistics, TU Dortmund University (2002-2004) Habilitation in Statistics, TU Dortmund University, Germany (April 15, 2013) Professor Trautmann's research program centers on cutting-edge topics in artificial intelligence and optimization. Her primary research interests include (Trustworthy) Artificial Intelligence, Machine Learning, Data Science, Automated Algorithm Selection and Configuration, Exploratory Landscape Analysis, (Multiobjective) Evolutionary Optimisation, and Data Stream Mining. She leads the Machine Learning and Optimisation research group at Paderborn University, which develops innovative approaches for understanding and improving optimization algorithms through landscape analysis and automated configuration techniques. Her work bridges theoretical foundations with practical applications, particularly in the domains of trustworthy AI and algorithm selection. Her extensive publication record reveals a clear trajectory toward increasingly sophisticated integration of deep learning with traditional optimization techniques. Recent work demonstrates a strong focus on multi-objective optimization problems, exploratory landscape analysis using deep learning methods, and the development of automated algorithm configuration systems. A notable trend is the application of transformer architectures to landscape analysis, as seen in her Deep-ELA work, which represents a significant innovation in the field. Her research consistently addresses the challenge of characterizing complex optimization problems to enable better algorithm selection and configuration. Professor Trautmann has received notable recognition for her scholarly contributions, including: GECCO Best Paper Award for "Deep reinforcement learning for instance-specific algorithm configuration" As an academic leader, Professor Trautmann has secured significant research funding for projects including "Towards Robustness of Disinformation Campaign Detection Algorithms in Open Online Media in the Context of Trustworthy AI" and "Automated rail transport as a backbone for sustainable, networked mobility in rural areas." She actively mentors students through her teaching of advanced courses in machine learning, optimization, and data science. Her industry connections, stemming from her previous work as an Analytics Consultant at Roland Berger Strategy Consulting, enable her to bridge academic research with practical applications. Professor Trautmann leads the Machine Learning and Optimisation research group at Paderborn University, which collaborates extensively with international partners. She is a key supporter of the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) and a member of the European Research Center for Information Systems (ERCIS). Her group maintains strong connections with research centers across Europe, particularly through her involvement with the Transregional Collaborative Research Centre 318.
Sebastian Krieter is a researcher in software engineering and product-line systems, affiliated with Otto-von-Guericke University Magdeburg, Germany. He completed his PhD in 2022 with a dissertation focused on efficient interactive and automated product-line configuration. His research spans software variability, feature modeling, configuration management, and automated software configuration. Research Interests: Software Product Lines and Variability Management Feature Modeling and Analysis Automated Configuration and Sampling Algorithms Combinatorial Testing and Feature Interaction Knowledge Compilation and SAT-based Analysis His recent work includes developing methods for T-wise interaction coverage, improving sampling efficiency in configurable systems, and integrating AI techniques into software variability management. He has contributed to the development of tools like FeatureIDE and UVL (Universal Variability Language). Scientific Contributions: Over 70 peer-reviewed publications in top-tier venues such as ICSE, SPLC, GPCE, and ASE. Key organizer of workshops including the 1st International Workshop on Reverse Variability Engineering (Re: Volution). Active contributor to open-source tools and datasets for benchmarking feature model analyses. Collaborations: He has collaborated extensively with Thomas Thüm, Gunter Saake, Thomas Leich, and a network of international researchers in software engineering and AI.
Nikolai Käfer is a doctoral student at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at Technische Universität Dresden. His research focuses on computational logic, artificial intelligence, and probabilistic reasoning. Algebraic and logical foundations of computer science Specializing in feature diagram sampling and probabilistic argumentation frameworks Contributing to software variability modeling and Bayesian network analysis Nikolai has published extensively in premier venues such as the International Working Conference on Variability Modeling of Software-Intensive Systems (VaMoS) and the ACM International Systems and Software Product Line Conference (SPLC). His research interests include: Variability modeling in software systems Probabilistic knowledge representation Formal methods and verification Bayesian network foundations Abstract argumentation frameworks Decision support under uncertainty His recent publications address challenges in feature diagram sampling, BDD compilation techniques for feature models, and theoretical foundations of probabilistic argumentation. Nikolai collaborates with leading researchers in computational logic and software engineering at TU Dresden. The ICCL team at TU Dresden emphasizes interdisciplinary research in logic-based AI and formal methods. Nikolai's work contributes to advancing these areas through rigorous theoretical analysis and practical tool development.
Adam Krafczyk is a researcher in the Software Systems Engineering (SSE) group at the University of Hildesheim's Institute of Computer Science. As part of the Faculty of Mathematics, Natural Sciences, Economics and Computer Science, he focuses on software product lines, static analysis, and variability modeling. His work bridges theoretical computer science with practical industrial applications, particularly in improving software configuration and variability management. Research Interests: - Variability-aware metrics for software product lines - Static analysis techniques for large-scale systems - Conversion of integer-based variability to propositional logic - Industrial IoT platforms and energy optimization (e.g., ReGaP project) - Generative AI applications in software development (GENIUS project) - Configuration mismatch detection in systems like Linux Notable Projects: GENIUS: Generative AI for the Software Development Lifecycle ReGaP: Energy optimization in manufacturing via AI/IIoT IIP-Ecosphere: Industrial IoT platform for AI integration MetricHaven: Tool for variability-aware quality metrics Awards: • Best Paper Award at SEAMS 2023 for 'Control Action Types - Patterns of Applied Control for Self-adaptive Systems' Professional Contributions: - Developed novel approaches for analyzing software product line variability - Collaborated with industry partners like Siemens and Bosch - Active in conferences like SPLC, SEAMS, and ETFA
