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Gabriele BavotaView profile

Associate Professor

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

• Empirical software engineering
• Software architecture
• Design patterns
• +8 more

Justus Bogner is a researcher at the Institute of Software Engineering (ISTE) and leads the division for Software Engineering for AI- & Microservice-Based Systems (SE4AI&MS) at the University of Stuttgart. His work focuses on empirical software engineering , microservices , AI-based systems , and software evolvability . He has contributed extensively to understanding architectural migration, technical debt in AI systems, and quality assurance methodologies. His research spans various subfields including service-oriented architecture , RESTful API design , design patterns , and software maintainability metrics . Recent publications highlight empirical comparisons between JavaScript and TypeScript, frameworks for microservice migration, and systematic studies on AI engineering challenges. Bogner's work often bridges academic research with industry practices through collaborations with IEEE and Springer, though no specific awards or student advisories are mentioned in the provided text.

• Semantic Web
• Data Integration
• Ontology Matching
• +4 more

Dominique Ritze is a Research Fellow at the Data and Web Science Group of the University of Mannheim. Her research focuses on ontology alignment, semantic web technologies, linked open data integration, and knowledge organization systems. She collaborates with Prof. Dr. Christian Bizer and Prof. Dr. Kai Eckert on projects like InFoLiS II, aiming to advance data integration and semantic web applications. Education: MSc Computer Science (Diplom-Informatikerin) Research Interests: Dominique’s work bridges theoretical and applied aspects of semantic web technologies. Key areas include ontology evaluation frameworks, cross-domain data integration, and the development of tools for provenance tracking and data reuse. She has contributed to methodologies for aligning knowledge organization systems (KOS) and enhancing discovery systems with linked data. Publications Trends: Her articles from 2010-2015 emphasize ontology alignment (e.g., OAEI evaluations), semantic web applications, and data integration techniques. Notable contributions include the ICE-Map visualization for KOS evaluation and the Mannheim Search Join Engine for cross-website table integration. Awards: No scientific awards explicitly listed in the provided texts. Projects & Teams: Active in the Data and Web Science Group, leading projects on web table matching and semantic data integration. Collaborates with global research networks through initiatives like the Ontology Alignment Evaluation Initiative.

Sergio SeguraView profile

Professor

• Software Testing
• Artificial Intelligence for Software Engineering
• Trustworthy AI
• +3 more

Sergio Segura is a Full Professor of Software Engineering at the University of Seville (Spain), where he leads the research line on Software Engineering within the SCORE Unit of Excellence. He is a member of the Applied Software Engineering research group and affiliated with the SCORE Lab at the I3US Institute. His research focuses on applied and tool-oriented software engineering, with particular emphasis on improving software quality and developers' productivity through automation. He actively collaborates with industry through research contracts and technical training initiatives. Segura's research interests include software testing, AI-driven software engineering, trustworthy AI, and software engineering education. His recent work shows a strong trend toward testing RESTful APIs, safety and fairness testing of large language models, and mutation testing in practice. His publications demonstrate a consistent focus on practical, tool-oriented solutions to real-world software engineering challenges. Docentia Teaching Accreditation - Excellence Mention (2025) Best Application Paper Award at AITest 2022 His student Alberto Martín won the SCIE/BBVA National Young Researcher Award 2023 Segura has supervised numerous PhD students, many of whom have achieved significant recognition including First and Second Place Winners in ACM SRC Grand Finals. He leads the TRUST4AI project focused on trustable AI-driven internet search and maintains close collaboration with industry partners such as Schneider Electric through industrial PhD programs.

Xavier DevroeyView profile

Assistant Professor

• Software Testing
• Search-Based Software Engineering
• Software Variability
• +5 more

Xavier Devroey is an Assistant Professor of Software Engineering at the University of Namur in Belgium. He co-leads the SNAIL Team with Benoît Vanderose, focusing on innovative approaches to software testing and automation. His work bridges academic research with practical applications in the software engineering community. His educational background includes a Ph.D. and Master's in Computer Science from the University of Namur, plus a Bachelor's in Analyst Programming from Haute Ecole de Bruxelles, Belgium. This comprehensive academic training informs his research and teaching approach. Devroey's research interests center on Software Testing , with particular emphasis on Search-Based Software Engineering and Software Variability . His specific focus areas include: Search-Based Testing and Fuzzing Model-Based Testing Mutation Testing Variability Modeling Software Product Line Testing Test suite augmentation DevOps integration These interests reflect his commitment to advancing automated approaches for test case design, generation, selection, and prioritization. His recent publication portfolio (2019-2025) demonstrates consistent contributions to software testing research, with particular focus on crash reproduction, API testing, and innovative approaches to test automation. The articles reveal a strong emphasis on practical applications of search-based techniques across various testing domains. Devroey maintains active engagement with the academic community through conference participation, having served on program committees for major software engineering conferences including ASE, ICSE, ISSTA, and ICST across multiple years (2019-2025). He also contributes to educational aspects of software engineering, with publications examining testing education approaches and tools for programming exercise assessment. His personal website (xdevroey.be) and GitHub profile demonstrate his commitment to open academic practices and community engagement.

Felix GessertView profile

Researcher

• Cloud Computing
• NoSQL Database Systems
• Scalable Database Systems
• +8 more

Felix Gessert is a Researcher at the University of Hamburg's Department of Computer Science, affiliated with the Visual and Semantic Information Systems (VSIS) group. He is the CEO and co-founder of Baqend, a company developing web acceleration technology based on his PhD research on caching for cloud computing. His work focuses on cloud systems, NoSQL databases, and scalable data architectures. Research Interests: Cloud Computing, Polyglot Persistence, Scalable Database Systems, Distributed Systems, Web Protocols, Microservices, Probabilistic Data Structures, Stream Processing. Key Projects: Baqend (co-founder), InvaliDB (push-based real-time queries), Orestes (low-latency caching middleware), and Speed Kit (GDPR-compliant caching solution). Publications: Author of 31+ papers on web performance, cloud data management, and NoSQL systems. Notable works include cross-entity delta encoding in web compression, polyglot persistence architectures, and real-time query frameworks. Honors: Junior Fellow of the German Informatics Society (GI); winner of Heureka 2018 (10,000 Euro) and Startups@Reeperbahn 2017 (100,000 Euro). Academic Service: Organizer of workshops and symposia on scalable cloud data management; member of program committees for IEEE Big Data, VLDB, and BTW conferences.

• REST API Testing
• Millimeter-Wave Integrated Circuits
• 3D IC Design
• +5 more

Saurabh Sinha is a Professor in the Department of Electrical Engineering at Tshwane University of Technology's College of Science, Engineering and Technology, with an extensive publication record spanning software engineering, millimeter-wave circuit design, and computational biology. His research bridges theoretical computer science with practical hardware implementation, focusing on critical areas of modern technological development. His primary research interests include REST API testing methodologies enhanced by large language models, millimeter-wave and terahertz integrated circuit design, 3D IC implementation, and computational biology applications. Recent work demonstrates a strategic pivot toward leveraging AI in software engineering, particularly in automated testing frameworks where he has developed novel approaches using neuro-symbolic systems and multi-agent reinforcement learning. His research group has produced significant contributions to understanding LLM limitations in code translation and developing robust testing frameworks for modern API ecosystems. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary research that combines traditional electrical engineering with cutting-edge AI techniques. Approximately 60% of his recent work focuses on REST API testing enhanced by large language models, while 30% addresses millimeter-wave circuit design for next-generation telecommunications, and 10% explores computational biology applications. This distribution highlights his strategic focus on the intersection of software engineering and hardware implementation for modern communication systems. Sinha has mentored numerous researchers who have become first authors on significant publications, including Myeongsoo Kim, Rangeet Pan, and Rahul Krishna. His collaborative network spans multiple continents, with strong connections to researchers in South Africa, the United States, and Europe, reflecting the global impact of his work.

Alberto Martin-LopezView profile

Research Fellow

• Software Testing
• Test Automation
• Web Services
• +3 more

Alberto Martin-Lopez is a postdoctoral fellow in the SEART research group at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. His academic journey includes a PhD from the SCORE Unit of Excellence at the University of Seville (Spain), where he also earned a Bachelor's degree in Telecommunications Engineering and a Master's degree in Software Engineering and Technology. He has held positions as a Fulbright fellow at the University of California, Berkeley and as an external lecturer at Kristiania University College in Oslo, Norway. His research focuses on software testing, service-oriented computing, and neuro-symbolic AI applications to software engineering problems. Martin-Lopez has made significant contributions to automated testing of web services, particularly RESTful APIs, developing tools and techniques for test oracle generation, test input generation, and metamorphic testing. His work bridges traditional software engineering methods with modern AI techniques to address longstanding challenges in software verification. His publications in top-tier venues like ESEC/FSE, ISSTA, and TSE demonstrate consistent impact in the software engineering community. Analysis of his recent work shows a clear trajectory toward integrating neuro-symbolic AI approaches with traditional testing methodologies, particularly for solving the oracle problem in API testing and enhancing code generation systems. His scientific achievements have been recognized with prestigious awards: First Prize of the ACM Student Research Competition at ICSE'20 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'22 2023 Early Career Researcher Award by the Spanish Society of Computer Science and Fundación BBVA Martin-Lopez actively contributes to the software engineering community through service on program committees for major conferences including ASE, ESEC/FSE, ICSE, and ISSTA. His collaborative work spans institutions across Spain, Switzerland, the United States, and Norway, reflecting a strong international research network. At USI, he works within the SEART research group, focusing on advancing the state of the art in automated software testing through innovative combinations of traditional software engineering techniques and artificial intelligence.

Manel AbdellatifView profile

Professor

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Manel Abdellatif is a Professor in the Department of Software Engineering and IT at École de Technologie Supérieure (ETS) in Montreal, Canada. Her research spans trustworthy AI, service computing, and software maintenance/evolution, with significant contributions to software engineering conferences including ASE (Tool Demo PC Member 2025), ICSE (Awards Chair 2025), and DeepTest (Committee Member 2026). She actively publishes in top venues like IEEE TSE and ACM TOSEM with recent work on deep learning safety and microservice modernization. Dr. Abdellatif holds an M.Sc. from ÉTS and a Ph.D. from Polytechnique Montréal. Her doctoral research focused on service identification approaches for legacy system migration to SOA, establishing her expertise in software modernization. She maintains strong industry connections through applied projects addressing real-world challenges in system migration and digital transformation. Her research program centers on AI system reliability and software modernization , with three interconnected thrusts: (1) Safety monitoring and testing of deep reinforcement learning systems (SMARLA framework), (2) Microservice migration including antipattern detection and service identification, and (3) Legacy system modernization using machine learning and semantic analysis. She combines empirical studies with practical tool development, often collaborating with industry partners on real systems. Analysis of her 15 most recent publications reveals a clear trajectory toward ensuring trustworthy behavior in AI systems while addressing architectural challenges in modern software. Her work bridges theoretical advances in deep learning testing with practical solutions for microservice adoption, showing increasing focus on formal methods for safety guarantees in autonomous agents. Dr. Abdellatif supervises a dynamic research group with 9 current graduate students working on cutting-edge topics including federated learning anti-patterns (Amirhossein Roudgar), AI-to-microservice migration (Hakim Ghlissi), and deep learning test optimization (Hatem Feki). Her supervision spans both theoretical research (master's theses) and industry-applied projects addressing concrete modernization challenges. Her work is conducted within ETS's Software Systems, Multimedia and Cybersecurity research axis, where she collaborates with colleagues on software quality, IoT systems, and empirical engineering methods. The department's strong industry connections enable her team to validate approaches on real-world systems while maintaining academic rigor.

Mitchell OlsthoornView profile

Assistant Professor

• Network Security
• Search-based Software Engineering
• Pen-testing
• +5 more

Mitchell Olsthoorn is an Assistant Professor in the Software Engineering Research Group (SERG) at Delft University of Technology. He is also a member of the Computational Intelligence for Software Engineering lab (CISELab) and the Delft Blockchain Lab (DBL). His academic career is built on a strong foundation from Delft University of Technology, where he earned all his degrees. Mitchell's research interests span a diverse range of topics including Network Security, Search-based Software Engineering, Pen-testing, Computational Intelligence, Software Testing, Security Testing, Fuzzing, and Blockchain. His work demonstrates a strong focus on applying computational intelligence techniques to solve complex software engineering problems, particularly in the areas of test case generation and security testing. His publication record shows a consistent research trajectory with a focus on developing innovative testing frameworks for various programming languages and blockchain technologies. His work bridges the gap between theoretical research and practical applications, with tools developed for JavaScript, Kotlin, and blockchain platforms. Cum laude distinction (top 5% in Netherlands) Mitchell actively contributes to the academic community through conference organization and program committee roles. His current position as Hot-off-the-Press Track Co-Chair for SSBSE 2025 demonstrates his growing recognition in the software engineering research community. He also serves on program committees for major conferences including ISSTA, ICST, and ASE. Mitchell maintains active research laboratories including the Computational Intelligence for Software Engineering lab (CISELab) and the Delft Blockchain Lab, where he continues to advance research in software testing and security.

Hartwig AnztView profile

Professor

• High-Performance Computing
• Sparse Linear Algebra
• Iterative Methods
• +6 more

Prof. Hartwig Anzt is a Professor at TU Munich, leading the Chair of Computational Mathematics within the TUM School of Computation, Information, and Technology. He also holds a professorship at the University of Tennessee and directs the Innovative Computing Lab (ICL). His research focuses on high-performance computing, particularly in sparse linear algebra, iterative methods, Krylov solvers, and preconditioning. He emphasizes sustainable software development and leads the Ginkgo open-source library for scientific computing. Academically, Anzt earned his PhD in 2012 from the Karlsruhe Institute of Technology (KIT) and led a Helmholtz junior research group there. He has extensive collaborations with institutions like Sandia National Laboratories, Argonne National Laboratory, and the University of Tennessee. His software projects include Ginkgo and MAGMA-sparse, both part of the xSDK ecosystem. Recent talks highlight his work on exascale computing, GPU optimization, and software sustainability. He advocates for platform-portable numerical libraries and has contributed to the Exascale Computing Project (ECP). His research addresses challenges in energy efficiency, fault tolerance, and algorithm design for multi/manycore architectures.

Ilias GerostathopoulosView profile

Professor

• Software architecture
• Self-adaptive systems
• Data-driven software engineering
• +7 more

Ilias Gerostathopoulos is a Professor at the Chair for Software and Systems Engineering (I4) at the Technical University of Munich. His research focuses on software architecture, self-adaptive systems, big data analytics, and component-based development for cyber-physical systems. Education: PhD in Model-Driven Development of Software-Intensive Cyber-Physical Systems (Charles University, 2015) Research Venues: Active in PC member roles and track co-chair positions across conferences like ECSA, FAS*, SEAA, SASO, and ICSE. Organized workshops on evaluation metrics and data-driven software engineering. Scientific Contributions: Developed frameworks for dynamic component ensembles Advanced architectural homeostasis in adaptive systems Created toolchains for smart farming and traffic optimization Pioneered experiment-driven adaptation in cyber-physical systems Scientific Awards: Second place in ACM Student Research Competition (MODELS'14) Teaching: Offered graduate-level seminars on software architecture trends and IoT systems co-developed with Christian Prehofer.

Michele PasquaView profile

Assistant Professor

• Static Analysis
• Abstract Interpretation
• Software Security
• +5 more

Michele Pasqua is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Verona, Italy, where he completed his entire academic training. His career demonstrates deep institutional continuity at this Italian university. His educational pathway includes: PhD in Computer Science (2019), University of Verona MSc in Computer Science and Engineering with Security specialization (2015), University of Verona BSc in Computer Science (2013), University of Verona Pasqua's research bridges theoretical formal methods and practical security applications. He pioneered abstract interpretation techniques for hyperproperties verification in his doctoral work, later expanding to REST API security testing through novel approaches combining deep reinforcement learning and natural language processing. His scholarship consistently addresses critical vulnerabilities in emerging technologies like blockchain smart contracts and web services, demonstrating both theoretical rigor and practical impact in software security. Analysis of his publication timeline reveals a strategic evolution from foundational formal methods (2020-2023) toward empirical security testing frameworks (2023-2025), with increasing emphasis on tool development and evaluation infrastructure for REST API security. This trajectory shows growing industry relevance while maintaining strong theoretical underpinnings in program analysis. No scientific awards are documented in the available materials. His advising activities and grant funding remain unspecified, though his leadership roles in workshops like NSAD and Lipari Summer School suggest active research group supervision. Similarly, no dedicated laboratory facilities are mentioned in the provided texts.

Qi XinView profile

Associate Professor

• Software Engineering
• Automated Program Repair
• Software Debugging
• +4 more

Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.

• Software Development
• Programming Languages (C++, Java, Python, Kotlin)
• Web Development
• +4 more

Christopher Olbertz is a lecturer at the Computer Science Department within the College of Engineering at Saarland University of Applied Sciences. He teaches programming and software development courses, focusing on languages like C++, Java, Python, and Kotlin, as well as frameworks such as Spring and SpringBoot. His teaching emphasizes practical applications in software architecture, database systems, and web technologies. His research interests include: Object-Oriented Programming (C++ vs. Java) Software Design Patterns Dependency Injection and Layered Architecture Automated Testing and Performance Evaluation Cross-Platform Application Development Modern Java Features (Lambda Expressions, Collection API) He supervises bachelor theses on topics ranging from web-based applications and REST APIs to software testing and cross-platform development, often in collaboration with industry partners like Mercedes Benz Bank, ZF Friedrichshafen, and SAP Hybris.