Large Language Models in Software Development Professors and Supervisors in Germany

Hongyu ZhangView profile

Professor

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.

Official University Profile

Qing HuangView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.

Official University Profile

Song WangView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering since May 2024. Previously, he served as an Assistant Professor at the same institution from July 2019 to May 2024. He earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan, an MS degree from the Chinese Academy of Sciences in June 2014 under Profs. Ye Yang and Wen Zhang, and BE and BHRM degrees from Sichuan University in June 2011. Dr. Wang's research focuses on the intersection of Software Engineering and Artificial Intelligence, with two main thrusts: 1) leveraging AI technologies to address software reliability challenges (AI for SE), and 2) developing software reliability techniques to improve AI infrastructure systems (SE for AI). His specific interests include software testing, program analysis, software reliability, and machine learning applications in software engineering. His research has led to tools that have detected hundreds of true bugs across various open-source projects. His recent publications reveal a strong focus on applying large language models to software engineering tasks, analyzing vulnerabilities in deep learning libraries, and developing techniques for software testing and reliability. The research spans multiple subfields including API testing, vulnerability detection, bias analysis in generated code, and automated assurance case generation. TOSEM Distinguished Reviewer Award 2023 APSEC'23 Distinguished Paper Award ACM SIGSOFT Distinguished Paper Award (ICPC 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2020) Best Paper Award at PROMISE 2019 Dr. Wang actively mentors students at all levels, currently supervising multiple PhD and MASc students. His research group has produced numerous publications in top-tier software engineering venues, including ICSE, FSE, ASE, and TOSEM. He also serves on the editorial board of ACM TOSEM and has been involved in organizing major conferences like ASE and CASCON.

Official University Profile

Henner GimpelView profile

Professor

Frankfurt University of Applied SciencesGermany

Professor Henner Gimpel holds the Chair of Digital Management at the University of Hohenheim, where he serves as Vice Dean for Research and Knowledge Transfer at the Faculty of Business, Economics and Social Sciences. He leads the Digital Business Management program, directs the FIM Research Institute for Information Management, and serves as academic director of the Digital Leadership Academy. Additionally, he holds a leading position at Fraunhofer FIT's Business Informatics branch. Professor Gimpel's research focuses on the analysis and design of digitalization, particularly examining socio-technical information systems where people, information, and digital technologies interact. His work emphasizes human-centered and economically effective development of information systems, with special attention to individual acceptance and use of digital technologies across various roles including consumers, customers, patients, employees, and users. His key research areas include Digital Health and Wellbeing, Hybrid and Artificial Intelligence, Smart Sustainability and Digital Ethics, and IT adoption and usage. His research methodology combines theoretically grounded empirical studies with design-oriented and model-theoretical approaches, bridging business administration (particularly business informatics, marketing, and organization) with behavioral economics, psychology, and computer science. Professor Gimpel has international research experience from stays at IBM T.J. Watson Research Center in New York and the Center for Interuniversity Research and Analysis on Organizations in Montréal. Current Research Projects: Influence of ICT on Collective Intelligence of Human Groups (DFG) ABBA: AI for Business | Business for AI (BMBF) PALLADiUM: Palliative Care as a Digital Working World (bidt) Hospital 4.0: Lean digital-supported logistics in hospitals (BMBF) ForDigitHealth: Research network for healthy digital technology use (StMWK) PräDiTec: Prevention for safe and healthy work with digital technologies (BMBF) DeLLFi: Integrating digitalization in teaching, learning and research Professor Gimpel actively supports numerous companies across various industries in developing innovative solutions for digital transformation challenges, with clients including Allianz, BMW, Carl Zeiss, Deutsche Bank, IBM, Microsoft, Siemens, and multiple university hospitals. Prior to his academic career, he gained five years of practical experience as a management consultant at McKinsey & Company.

Official University Profile

Dieter LandesView profile

Professor

Coburg University of Applied SciencesGermany

Prof. Dr. Dieter Landes serves as a Professor in the Faculty of Electrical Engineering and Computer Science at Coburg University of Applied Sciences, Germany. His research bridges theoretical AI advancements with practical cybersecurity and energy solutions, particularly targeting small and medium enterprises (SMEs) through federally funded initiatives. Landes' core research interests include: Cybersecurity applications of Large Language Models for Blue/Red Teaming operations AI-driven predictive maintenance in photovoltaic energy systems Realistic data generation for security system training and testing Intelligent educational technologies for personalized learning navigation High Performance Computing infrastructure for applied AI workloads His current grant portfolio demonstrates sustained research leadership: Blue-and Red-Agent-based Cyber Security (2025-2028): Federal Office for Information Security project developing LLM-powered defenses against SME-targeted cyberattacks Kick-PV (2023-2026): AI characterization methods for remote photovoltaic system diagnostics GENESIS (2022-2025): Framework for generating realistic data flows to train insider threat detection systems VoLL-AI (2021-2025): Multi-university collaboration (Erlangen/Bamberg) on AI-driven learning navigation HPC4AAI (2021-2024): Infrastructure development for applied artificial intelligence computing Landes operates within interdisciplinary research collectives at Coburg University, frequently partnering with Friedrich-Alexander University of Erlangen-Nuremberg and Otto-Friedrich University of Bamberg. His project pipeline extending to 2028 confirms active research trajectory in applied computer science.

Official University Profile