Automatic Software Testing Professors and Supervisors in Germany

Martin Eberleinمشاهده پروفایل

پژوهشگر

Humboldt UniversityGermany

Martin Eberlein is a Doctoral Researcher and Ph.D. candidate in the Software Engineering group at Humboldt University of Berlin, Germany, working under Prof. Lars Grunske. Previously, he collaborated with Prof. Andreas Zeller's research group at CISPA Helmholtz Center for Information Security. His research focuses on automated software engineering, particularly software testing, vulnerability detection, and fuzzing. He is currently part of the EMPEROR project, which aims to automatically generate explanations for program behaviors, especially failures. Dr. Eberlein has developed notable tools including Avicenna (for explaining why programs fail) and EvoGFuzz (an evolutionary, grammar-based fuzzer). Education: 2022-Present: Dr. rer. nat. (PhD), Computer Science, Humboldt-Universität zu Berlin 2020-2022: Master of Science (M.Sc.), Computer Science, Humboldt-Universität zu Berlin (Final Grade: 1.0) 2015-2020: Bachelor of Science (B.Sc.), Computer Science, Humboldt-Universität zu Berlin (Final Grade: 1.3) His publications demonstrate innovative approaches to software testing and debugging, with emphasis on explaining program failures through machine learning. Recent work includes 'Which Inputs Trigger My Patch?' (APR'2025) and 'jAST' (FSE'2025), showing trends toward automated explanation of software behavior and program analysis tools. Scientific Awards: Black Shirt from HU-Berlin research group (2025) €900k initial funding for InputLab startup Dr. Eberlein serves on program committees for FSE 2025 (Artifact Evaluation Track) and ISSTA 2025 (Tools Demonstrations Track). He organizes and assists with teaching courses at HU Berlin, particularly in Software Engineering II and Compiler Construction. As co-founder of InputLab, he bridges academic research with practical applications in software testing for government and business formats. He maintains active research collaborations through GitHub, his blog, and participation in major software engineering conferences worldwide.

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Xiaobin Chenمشاهده پروفایل

استادیار

University of TübingenGermany

Artificial Intelligence in Education
Intelligent Computer Assisted Language Learning (ICALL)
Second Language Acquisition
+۷ مورد دیگر

Xiaobin Chen is a Junior Professor at the University of Tübingen, affiliated with the Hector Institute for Empirical Educational Research within the Faculty of Humanities and the Department of Social Sciences. He leads research at the intersection of artificial intelligence and language education, with strong institutional support from the German Federal Ministry of Education and Research (BMBF). His work is deeply collaborative, involving partnerships with researchers across institutions and active participation in the LEAD Graduate School & Research Network. His research focuses on leveraging artificial intelligence and natural language processing to enhance second language learning. Key areas include Intelligent Computer-Assisted Language Learning (ICALL), automated language assessment, readability modeling, and the design of pedagogically sound AI tools such as conversational agents and interactive reading platforms. His work emphasizes aligning technological innovation with educational theory and classroom needs. The recent publications reflect a strong trajectory in AI-driven educational research, particularly in applying NLP to analyze learner language, develop intelligent tools, and assess language proficiency. Themes across the articles include individual differences in learning, human-AI collaboration, automated scoring, and the integration of AI into real-world educational settings, especially in EFL contexts. Xiaobin Chen leads two major BMBF-funded research projects: Aisla - Intelligent Language Assistant for Learning English in Everyday Situations (2021–2024) AI in Education: Pedagogically Oriented Language Knowledge Extraction and Readability-Controllable Natural Language Generation (2022–2025) These projects support his team’s development of innovative, theory-informed AI systems for language learning and assessment. He is associated with the KIBi Research Group , which focuses on integrating knowledge, interaction, and behavior in intelligent educational systems. The group develops and evaluates digital tools that adapt to learners’ needs, using data from interaction logs and linguistic analysis to drive personalization.

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Christoph Haaseمشاهده پروفایل

دانشیار

Max Planck Institute for Software SystemsGermany

Christoph Haase is an Associate Professor at the Department of Computer Science, University of Oxford, and a Fellow of St Catherine’s College. His research focuses on developing rigorous mathematical methods for algorithmic verification, automated reasoning, automata theory, and logic in computer science. University of Oxford, UK (Current) University College London, UK (Former) ENS Paris-Saclay, France (Former) Microsoft Research Cambridge, UK (Former) His work includes fundamental contributions to decision procedures for arithmetic theories, particularly in Presburger and Büchi arithmetic, and applications to verification of software and hardware systems. He leads the ARiAT project, funded by an ERC Starting Grant (2020–2025), aiming to advance quantifier elimination and complexity bounds for arithmetic theories. Key publication trends include verification , automata theory , arithmetic logic , computational complexity , and automated reasoning . Collaborations span institutions like University of Paris, UCL, and Microsoft Research. Scientific Awards : ERC Starting Grant (2019) EPSRC Doctoral Prize (during DPhil studies) He has supervised PhD and MEng projects on topics such as SAT solving, linear arithmetic, and matrix semigroups, often co-supervising with researchers like Stefan Kiefer and James Worrell. Teaching includes Logic and Proof at Oxford and Operating Systems at ENS Paris-Saclay.

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Ulrich Konigorskiمشاهده پروفایل

استاد

Technical University of DarmstadtGermany

Prof. Dr.-Ing. Ulrich Königorski is a Professor of Control Systems and Mechatronics at the Technical University of Darmstadt's Institute of Automatic Control and Mechatronics. He holds a doctorate from TU Karlsruhe and has extensive industry experience at Robert Bosch GmbH and Thyssen Henschel. His research focuses on modeling nonlinear systems, mechatronic systems control, vehicle dynamics, and microgrid control. Königorski leads the Control Systems and Mechatronics Laboratory and has contributed to projects like Proreta 3 (driver assistance systems) and FLORIDyn (wind turbine modeling). His work bridges theoretical control methods with practical applications in automotive, robotics, and renewable energy sectors. Education: Electrical Engineering (1977-1983, TU Karlsruhe), PhD (1987, TU Karlsruhe) on state feedback design. Professional roles include Dean of the Faculty of Electrical Engineering and spokesperson roles in academic committees. Research interests emphasize system modeling, robust control, and mechatronics applications. Notable areas include automated driving trajectory planning, sensorless motor control, and inverter-based microgrid synchronization. His recent work addresses challenges in unsupervised scenario extraction for autonomous systems and gait phase estimation for prosthetics. Labs/Teams: Head of the Control Systems and Mechatronics Laboratory. Collaborations include industry partnerships for automotive and renewable energy projects. Ongoing research focuses on enhancing safety in nonlinear systems, optimizing energy grids, and advancing human-centric automation technologies.

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Yun Linمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Yun Lin is an Associate Professor and Deputy Head of the Department of Computer Science and Technology at Shanghai Jiao Tong University's School of Computer Science. Prior to joining SJTU, Lin served as a Research Assistant Professor at the National University of Singapore working with Prof. Dong Jin Song. Lin leads the CoPhi ("Code Philia") research group, which focuses on the intersection of Software Engineering, AI, and Security. Lin's research spans three major areas: Automatic Programming (including code editing, software testing, and debugging), Explainable AI (focusing on representation interpretation and training data attribution), and Web Misinformation (particularly phishing and scam detection). The research has resulted in numerous tools including CoEdPilot for code editing recommendation, DeepDebugger for interactive debugging of deep classifiers, and Phishpedia for phishing webpage detection. Lin's recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models and vision language models, with traditional software engineering and security tasks. The work shows increasing sophistication in understanding project context, handling interactive nature of programming tasks, and addressing security challenges in the age of generative AI. Key themes include consistency-based approaches for anomaly detection, agent-based frameworks for complex tasks, and hybrid models that combine symbolic reasoning with neural approaches. ACM Distinguished Paper Award in ICSE'18 for "Towards Optimal Concolic Testing" Distinguished Reviewer Award in FSE'25 2nd prize Research Prototype Award in ChinaSoft'24 Lin advises a large team of PhD, Master's, and undergraduate students, with several publications co-authored with students appearing in top venues. Current research is supported by collaborations with National University of Singapore, particularly with Prof. Dong Jin Song, and includes projects on code editing, GUI testing, and phishing detection. The CoPhi group maintains active development of multiple research tools and datasets. The CoPhi research group under Lin's leadership focuses on building practical tools that bridge the gap between theoretical advances and real-world programming and security challenges. The group's work spans from fundamental program analysis techniques to applied security solutions, with an increasing emphasis on leveraging AI capabilities while maintaining explainability and reliability.

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Yintong Huoمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Yintong Huo is an Assistant Professor at the School of Computing & Information Systems, Singapore Management University (SMU), where he leads research in intelligent software engineering. He received his PhD from The Chinese University of Hong Kong (CUHK) in 2024 under Prof. Michael R. Lyu and holds a Bachelor's degree from the University of Electronic Science and Technology of China. His research focuses on empowering AI models (particularly LLMs) for software development, testing, and operations, with two flagship projects: LogPAI (open-source AI platform for automated log analysis) and WebPAI (multimodal intelligence for automatic webpage development). His work spans log analysis, code intelligence, UI generation from prototypes, and configuration diagnostics. Huo's publication record shows strong trends in leveraging multimodal LLMs for practical software engineering challenges, with recent work on interactive webpage generation (Interaction2Code), configuration logging (ConfLogger), and log parsing (LILAC). His research bridges theoretical AI advancements with real-world system reliability needs. ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022) ACM SIGSOFT CAPS Travel Grants National Scholarship (2019) Huo actively supervises PhD students (including Shi Ying Chang and Dan Huang) and research engineers. His lab has secured funding for multiple projects including WebPAI and LogPAI. He serves on program committees for major conferences (ASE, ICSE, FSE) and reviews for top journals. Current projects include dynamic webpage generation and configuration diagnostics, with ongoing work on small language models for logging systems. Huo leads the LogPAI and WebPAI research groups, developing open-source tools for automated log analysis and multimodal UI code generation. The LogPAI project has garnered over 3,000 GitHub stars and 70,000 downloads. His team collaborates with industry partners on AIOps challenges and is expanding into configuration diagnostics through the ConfLogger project.

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Hui Liuمشاهده پروفایل

استاد

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.

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Antonio Ruiz-Cortésمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Antonio Ruiz-Cortés is Professor of Software and Service Engineering at the University of Seville and an elected member of the Academy of Europe. He currently heads the Applied Software Engineering Group at the University of Seville and has served as President of the Spanish Society on Software Engineering (SISTEDES) since September 2018. His research spans multiple critical areas of modern software engineering: Service-Oriented Computing Software Architecture Business Process Management Testing methodologies Software Product Lines Automatic Model Analysis Professor Ruiz-Cortés has established himself as a leading researcher in REST API testing and verification, with multiple influential publications in top software engineering conferences. His work bridges traditional software engineering with emerging domains like quantum computing, demonstrating remarkable adaptability. He has developed practical tools including RESTest and AGORA that address real-world testing challenges faced by industry practitioners. His scientific contributions have been recognized with the Most Influential Paper award at SPLC 2017. He serves as associate editor for Springer Computing and has organized major conferences including JCIS 2008, SPLC 2017, SISTEDES 2018 and BPM 2020, demonstrating significant leadership in the academic community. Professor Ruiz-Cortés maintains active research funding supporting his work in service-oriented computing and software testing, though specific grant details aren't provided in the available information. His presidency of SISTEDES reflects his commitment to advancing software engineering research in Spain.

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Austin Mordahlمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Austin Mordahl is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research focuses on improving the usability and reliability of software quality assurance tools, particularly static analyses and fuzz testing. Dr. Mordahl actively seeks students interested in cutting-edge research in software engineering at UIC. Dr. Mordahl's research interests span multiple areas of software engineering with a particular emphasis on: Static Analysis and Taint Analysis for software security Automated Testing techniques including fuzz testing Improving the usability and reliability of software quality assurance tools Configurable static analysis tools and their behavior Applying machine learning to triage and configure static analysis tools Lifting static analysis to work on software product lines Improving evaluations of fuzz testing tools His recent publications demonstrate a strong focus on addressing challenges in configurable static analysis tools, with particular attention to nondeterministic behavior, configuration spaces, and automatic testing and debugging approaches. Mordahl's work bridges theoretical foundations with practical applications, aiming to make software quality assurance tools more accessible and effective for developers. Dr. Mordahl has received several prestigious awards for his work: NSF Graduate Research Fellowship Awardee (2020) Eugene McDermott Graduate Research Fellowship Awardee (2020) ICSE 2019 Student Research Competition Winner Dr. Mordahl is actively involved in mentoring and seeks students interested in software engineering research. He has served on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and PLDI. His service to the academic community extends to artifact evaluation committees, demonstrating his commitment to research reproducibility and rigor.

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Simone Scalabrinoمشاهده پروفایل

استادیار

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.

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Ajitha Rajanمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

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Sébastien Bardinمشاهده پروفایل

پژوهشگر

Max Planck Institute for Security and PrivacyGermany

Sébastien Bardin is a Senior Researcher and CEA Fellow at the Software Safety and Security Laboratory of Commissariat à l'Energie Atomique (CEA) in Paris-Saclay, France, with affiliation to University Paris-Saclay. He leads significant research initiatives including the BINSEC group on binary-level security analysis (since 2012) and the QBricks group on quantum program verification (since 2020), and served as Head of the Software Quantum Program at CEA LIST from 2021-2023. His research focuses on formal methods and automatic program analysis with applications in software security. Key areas include binary-level security analyses such as vulnerability detection & assessment, reverse engineering, and malware deobfuscation, as well as quantum programming and verification. His methodological expertise spans symbolic execution, abstract interpretation, software model checking, and SMT solving. Bardin's publications demonstrate consistent impact across top venues in security and formal methods, with notable papers at ESOP, CAV, ICSE, NDSS, and S&P. His work on robust symbolic execution, directed fuzzing, and binary-level analysis has established him as a leading researcher in security-oriented program analysis. Recent publications show growing emphasis on quantum program verification alongside continued contributions to binary security analysis. Scientific Awards: ICSE 2021 ACM SIGSOFT Distinguished Paper Award RTAS 2021 Best Paper Award CAV 2021 Selected Paper CEA Fellow (2021-present) ACM Senior Member (2021-present) Bardin actively mentors PhD students and has advised numerous researchers who have received recognition including GDR Sécurité and GDR GPL PhD awards. His service to the community is extensive, with leadership roles in GDR Sécurité, RESSI, and organizing major conferences including CAV 2023 (sponsor co-chair) and FIC 2023 (scientific program chair). He also contributes to multiple program committees across security and formal methods venues.

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