Object-oriented Software Engineering Professors and Supervisors in Germany

Shing-Chi CheungView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

Official University Profile

Hui LiuView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.

Official University Profile

Gary TanView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.

Official University Profile

Jie LuView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Jie Lu is an Associate Professor at the Institute of Computing Technology of the Chinese Academy of Sciences (ICT, CAS), where he leads research in software security and program analysis. His work focuses on developing advanced program analysis techniques to improve software reliability and security, with applications in cloud systems, distributed environments, and modern web applications. Dr. Lu's research interests include: Software Security: Focusing on vulnerability detection and prevention in open-source software Program Analysis: Specializing in static/dynamic analysis techniques and context-sensitive pointer analysis Cloud Systems: Researching distributed system security, crash-recovery, and concurrency bug detection His recent publications demonstrate a strong focus on practical security solutions for real-world systems. The research spans Kubernetes ecosystems, PHP applications, Linux kernel security, Java web applications, and Windows IPC systems. A notable trend is the development of precise static analysis techniques that balance efficiency with accuracy, addressing the longstanding challenge in program analysis. His work often bridges theoretical advances with practical implementations that have been adopted by industry. Dr. Lu has received several prestigious awards: ACM SIGSOFT Distinguished Paper Award 2025 Best Paper Honorable Mention at CCS 2022 Chinese Academy of Sciences Outstanding Doctoral Dissertation 2021 Chinese Academy of Sciences President's Special Award 2020 ICT New Hundred Stars 2020 Dr. Lu actively mentors students and researchers, recruiting PhD candidates, Master students, and research interns interested in software security and program analysis. His research has been supported by the National Natural Science Foundation of China, CCF-Huawei Innovation Research Plan, and CCF-Ant Research Fund. The Program Analysis Group (ICT-PAG) at the National Key Laboratory of Processor has successfully identified numerous errors and vulnerabilities in popular open-source applications, with over 200 severe bugs confirmed by the open-source community and assigned more than 100 CVE numbers. His research group, the Program Analysis Group (ICT-PAG), is based in the National Key Laboratory of Processor at ICT, CAS. The group has achieved significant impact through both academic publications in top venues (SOSP, CCS, USENIX Security, NDSS, OOPSLA, ISSTA, FSE, ASE, TSE) and practical applications in leading IT companies and government organizations.

Official University Profile

Michael HiltonView profile

Teaching Professor

Max Planck Institute for Security and PrivacyGermany

Michael Hilton is an Associate Teaching Professor at Carnegie Mellon University's School of Computer Science, where he serves as the Associate Department Head for Education in the Software and Societal Systems Department. He directs the Software Engineering Minor and the Software Engineering Concentration programs, and teaches various software engineering courses including Foundations of Software Engineering, Introduction to Software Construction, and Crafting Software. His research interests focus on improving the lives of software engineers, with particular emphasis on flaky tests , continuous integration , and structured editors . His work bridges both the research and educational aspects of software engineering, aiming to make software development processes more reliable and efficient while educating the next generation of software engineers. Analysis of his recent publications reveals a strong focus on flaky test detection and mitigation, with significant contributions to understanding test reliability in continuous integration environments. His research spans empirical studies of testing practices, machine learning approaches to flakiness prediction, and educational aspects of software engineering. A notable trend is his increasing focus on the intersection of software testing and education, particularly examining how students interact with modern development tools and practices. Professor Hilton is highly active in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and SIGCSE. He has contributed to numerous workshops and symposia focused on software engineering education and testing research. As an educator, he has developed and taught multiple software engineering courses covering object-oriented design, testing, concurrency, and software construction principles. He emphasizes practical, hands-on learning experiences that prepare students for real-world software development challenges. His educational research explores best practices for teaching software engineering and the impact of new technologies like generative AI on software engineering education. He maintains an active research lab focused on software engineering tools and education, collaborating with both academic and industry partners to address practical challenges in software development workflows. His current projects include investigations into flaky test patterns, continuous integration practices, and the integration of modern development tools into software engineering curricula.

Official University Profile

Simone ScalabrinoView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.

Official University Profile

Lian LiView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Lian Li is a Professor in the Institute of Computing Technology at the Chinese Academy of Sciences, where he leads the program analysis research group. He holds a PhD from the University of New South Wales, Australia, and a Bachelor's degree from Tsinghua University in Engineering Physics. His research focuses on developing innovative program analysis techniques and tools to enhance software reliability and security. His educational background includes a PhD in Computer Science from the University of New South Wales (2003-2007) with a thesis on "ScratchPad Management for Static Data Aggregates" under Professor Jinling Xue, and a Bachelor's degree in Engineering Physics from Tsinghua University (1993-1998). Lian Li's research primarily centers on program analysis techniques, particularly static analysis methods for software security and reliability. His group developed Wukong, a static analysis and detection system capable of identifying deep security vulnerabilities across functions, components, and complex dependencies in C/C++, Java, and Android applications. This tool has discovered thousands of errors in popular open-source software including Google Chromium, Bash, sed, and Hadoop, with hundreds confirmed by developers and over 50 CVEs assigned. His publication record shows a strong focus on program analysis, particularly context-sensitive pointer analysis, taint analysis, and vulnerability detection. His recent work (2021-2024) demonstrates continued innovation in context-free language reachability, efficient IFDS algorithms, and specialized analysis for generics and authorization vulnerabilities. His research spans cybersecurity, programming languages, and software engineering domains, with emphasis on practical applications for real-world software systems. ASE 2019 Distinguished Paper Award CCS 2022 Best Paper Honorable Mention Lian Li has guided numerous PhD and Master's students in computer system architecture and software theory. His research group maintains active collaborations across various software analysis domains, with funding supporting their work on tools like Wukong. They have developed significant intellectual property including multiple patents related to program analysis techniques. The program analysis research group he leads focuses on developing practical tools for software reliability and security. Their work bridges theoretical program analysis with real-world applications, particularly through the Wukong analysis system which has been successfully applied to major open-source projects.

Official University Profile