Search Engine Optimization Professors and Supervisors in Germany

Stefan Wagnerمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Stefan Wagner is a full professor of software engineering at the Technical University of Munich (TUM), where he leads the Chair of Software Engineering within the Informatics Heilbronn 3 department. He earned his PhD in computer science from TUM and has established himself as a leading researcher in software engineering with a unique interdisciplinary background combining computer science and psychology. Dr. Wagner's research spans multiple areas of software engineering including software quality, human factors, AI-based software engineering, automotive software, and empirical studies. His work often bridges theoretical foundations with practical applications, particularly in the automotive domain and AI-assisted development. He has published over 130 peer-reviewed scientific articles and authored the book 'Software Product Quality Control.' His recent publications demonstrate a growing focus on AI integration in software engineering, with particular attention to large language models, empirical validation of AI-assisted development practices, and the human aspects of these emerging technologies. There's also a strong thread of research on automotive software systems, technical debt, and software quality measurement. 2022 Class of IEEE Computer Society Distinguished Contributors Senior member of IEEE and ACM Best Full Paper Award, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement 2020 Best Paper Award, EAI International Conference on Cognitive Computing and Cyber Physical Systems 2020 Keynote at the International Conference on Software Quality 2020 Google Research Award 2009 Dr. Wagner actively mentors students and early-career researchers, with several frequent co-authors on his publications likely representing his advisees. His research has been supported by various funding sources enabling his work on automotive software, AI in software engineering, and empirical studies. He serves on the editorial boards of IEEE Software, Empirical Software Engineering, and the Journal of Systems and Software, contributing significantly to the academic community. His research group appears to focus on several key areas including AI-assisted software development, automotive software systems, and empirical software engineering methods. The group maintains active collaborations with industry partners, particularly in the automotive sector, and engages in interdisciplinary research bridging computer science with psychology and human factors.

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Masud Rahmanمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Software Engineering
Artificial Intelligence for Software Engineering (AI4SE)
Software Maintenance and Evolution
+۱۱ مورد دیگر

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

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Yutian Tangمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Dr. Yutian Tang serves as an Assistant Professor (UK Lecturer) and Principal Investigator at the School of Computing Science, University of Glasgow, where he supervises PhD students and leads research in AI-driven software engineering. His academic journey includes a PhD from The Hong Kong Polytechnic University's Department of Computing. His research spans AI+SE integration , particularly focusing on Large Language Models for program analysis, software testing, and Android security. Key areas include: LLM-assisted vulnerability detection and repair Empirical studies of real-world software systems Privacy protection mechanisms Configuration compatibility in mobile applications Smart contract security optimization His publication portfolio shows a clear trajectory toward AI-augmented software engineering , with recent work demonstrating how LLMs can enhance taint analysis, binary code similarity detection, and test generation. This evolution reflects the field's broader shift toward AI integration while maintaining rigorous empirical validation. Award highlights include: Best Industry Paper Award at ISSRE'18 Elevation to IEEE Senior Member (2024) Three Android OS defects confirmed by Google Security Team As an active researcher and community contributor, Tang serves on 40+ program committees including PLDI, ICSE, and FSE. His work receives funding from National Natural Science Foundation of China, Shanghai Science Commission, OpenAI, and Google. Current projects focus on automated bug localization and LLM-based testing frameworks, with recent grants from OpenAI Cybersecurity and Google Cloud programs. He leads research groups investigating Android security and AI-assisted program analysis, collaborating with institutions like Lund University.

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Chaiyong Ragkhitwetsagulمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Chaiyong Ragkhitwetsagul is an Assistant Professor at the Faculty of ICT, Mahidol University, Thailand, and co-founder of the SERU research group. His research focuses on software engineering with emphasis on maintenance, evolution, and program comprehension. Education: PhD in Software Engineering, University College London (UCL) MS in MSIT-Very Large Information Systems, Carnegie Mellon University BE in Computer Engineering, Kasetsart University His work investigates code proficiency metrics for Python and JavaScript to improve maintenance practices, employs search-based techniques for multi-objective clone configuration, and pioneers autorepairability as a novel software quality dimension. Recent publications demonstrate strong empirical focus on developer productivity and code quality assessment. 2024 publications reveal a cohesive trend toward practical tool development (e.g., jscefr) and empirical validation of software quality characteristics, bridging theoretical research with real-world engineering challenges. Active across major software engineering venues (ASE, ICSE, ICSME), he contributes to the community through program committees while leading the SERU research group's mission to advance software engineering science at Mahidol University.

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Hongyu Zhangمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

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Andrea Arcuriمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Andrea Arcuri is a Professor of Software Engineering at Kristiania University College in Oslo, Norway, where he leads the AISE lab. Since 2020, he also holds a part-time position as Adjunct Professor at Oslo Metropolitan University. After 5 years in industry (WesternGeco and Scienta/Telenor), he returned to academia full-time in October 2016. His research primarily focuses on automated software testing and search-based software engineering. Prof. Arcuri's research interests center around Software Testing , Search-Based Software Engineering , and Automated Testing techniques. He has made significant contributions to the field through the development of testing tools like EvoMaster for system-level test generation and EvoSuite for unit test generation. His work bridges theoretical foundations with practical applications, particularly in RESTful API testing and enterprise systems. His recent publications show a strong trend toward practical applications of search-based testing in industry settings, with particular emphasis on RESTful API testing, fuzz testing, and integration with modern development practices like DevOps. The research spans both theoretical foundations of search-based testing and practical tool development. ACM SIGSOFT Impact Paper Award 2023 for EvoSuite ICST 2022 10-Year Most Influential Paper Award ICSE 2021 10-Year Most Influential Paper Award Ranked 2nd Most Active Early Stage SE Researcher in JSS'18 Multiple Best/Distinguished Paper awards at SSBSE, ASE, and ISSTA Prof. Arcuri actively supervises PhD students and post-docs, including Iva Kertusha, Susruthan Seran, and Onur Duman. He serves in leadership roles for major conferences, including as FSE 2025 Artifact Track Chair and ICST 2022 Test Tool and Demo Track Chair. His research is supported through various academic grants and industry collaborations. He leads the AISE (Artificial Intelligence and Software Engineering) lab at Kristiania University College, where his team develops and maintains the EvoMaster system test generation tool. The lab focuses on practical applications of search-based software testing in real-world enterprise settings.

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Ralf Brunsمشاهده پروفایل

استاد

Hannover University of Applied Sciences and ArtsGermany

Prof. Dr. Ralf Bruns is a full-time Professor in the Department of Computer Science at the Faculty of Business and Information Technology, Hannover University of Applied Sciences and Arts. His office is located at Ricklinger Stadtweg 120, 30459 Hanover, with direct contact available via phone (+49 511 9296 1817) and email. His research focuses on cutting-edge computational methodologies including: Real-time data stream processing systems Bio-inspired algorithms ( evolutionary and swarm intelligence ) Machine learning applications in enterprise systems Semantic Web technologies for knowledge representation Software architecture patterns for event-driven systems Complex Event Processing (CEP) frameworks His publications demonstrate a consistent focus on event-driven architectures applied to logistics, healthcare, IoT, and urban mobility systems. Recent work emphasizes agent-based modeling and real-time analytics for decision support in dynamic environments. Prof. Bruns leads two key research initiatives: the Software Architecture Working Group (AG SWA) and the Smart Data Analytics Research Cluster . He also serves as faculty representative in the Fachbereichstag Informatik (FBTI) and contributes to academic selection committees.

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