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Thorsten Riemke-GurzkiView profile

Professor

University of Applied Sciences MediaGermany

Thorsten Riemke-Gurzki is a fulltime Professor for Web Technology, Corporate Portals & Usability at Stuttgart Media University in Stuttgart, Germany, where he has been working since 2008. Prior to his academic career, he worked as a freelance and employed consultant for various companies including SAP Germany. He describes himself as having 'grown up with hardware, CP/M, mailbox systems' and being 'web addicted since 1991,' positioning himself as both a frontend and backend developer and a 'web technology evangelist.' Professor Riemke-Gurzki's research interests span a comprehensive range of web technologies: Frontend and backend web development HTML, CSS, JavaScript and modern frameworks (React, React Native, Vue.js) Server-side technologies including Node.js, Go, Docker, and database systems Microservices architectures and single page applications Web 3D/AR, AI applications for the web, and Internet of Things User experience, usability, and content management systems His recent publications demonstrate a strong focus on practical web development techniques, covering topics from CSS fundamentals to advanced server implementation in Go and exploration of emerging technologies like JavaScript-based microcontrollers. His work bridges academic research with practical industry applications through his website riemke.dev where he shares technical articles and hosts a podcast on web development topics. Professor Riemke-Gurzki maintains an active presence in both academic and developer communities, with educational materials on GitHub and engagement with industry technologies like the Daimler UXLab truck. His approach emphasizes hands-on learning and practical application of web technologies.

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Preetha ChatterjeeView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Dr. Preetha Chatterjee is an Assistant Professor in the Department of Computer Science at Drexel University's College of Engineering, where she leads the SOftware Engineering and Analytics Research (SOAR) Lab. Her academic career spans software engineering research, teaching, and service, with a focus on improving developer productivity through advanced analytics and tools. Dr. Chatterjee earned her M.S. and Ph.D. in Computer Science from the University of Delaware, advised by Dr. Lori Pollock, following 5+ years of industry experience as a Software Engineer. Her educational background bridges practical industry experience with rigorous academic training. Her research focuses on Software Engineering with emphasis on developing tools, knowledge sources, and strategies to support software maintenance and improve developer productivity. She incorporates evidence from mining software repositories, conducting empirical studies, and adapting state-of-the-art techniques from Natural Language Processing and Machine Learning. Her current research directions include LLM-assisted software development and maintenance, developer collaboration in distributed software teams, and knowledge extraction from large-scale software artifacts. She has made significant contributions to emotion mining in developer communications, toxicity detection in open source projects, and trust dynamics in GitHub pull requests. Dr. Chatterjee's publications demonstrate a clear progression from foundational work on mining developer chat communications and code snippets toward more sophisticated applications of machine learning and large language models in software engineering contexts. Her recent work increasingly focuses on the intersection of software engineering with social aspects like emotions, trust, and toxicity in developer interactions. Distinguished Reviewer Award at FSE 2023 Drexel CCI Research Excellence Award (awarded to her lab member Ramtin Ehsani) Drexel CS Leadership Award (awarded to her lab member Amirali Sajadi) Dr. Chatterjee has advised numerous students at various levels, including Ph.D., M.S., and undergraduate researchers. Her SOAR Lab currently includes Ph.D. students Ramtin Ehsani and Amirali Sajadi, who have received significant recognition for their work. She has served on multiple program committees for major software engineering conferences including ICSE, FSE, ASE, and MSR, and has held leadership roles such as Tutorials Co-chair for MSR 2025 and Journal-first Co-Chair for ICPC 2024. She co-leads the Drexel Programming Systems Seminar and has been an editorial board member for the Journal of Systems and Software. The SOAR Lab focuses on innovative research at the intersection of software engineering, machine learning, and natural language processing. The lab has produced influential datasets like DISCO (Discord Chat Conversations for Software Engineering Research) and comprehensive annotated datasets of GitHub issue threads. Current projects include improving LLM-assisted bug resolution, security assessment of LLM-generated code, emotion mining from software engineering communication, and information extraction from developer chat conversations.

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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.

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Zhou YangView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Zhou Yang is an Assistant Professor at the University of Alberta and Fellow at the Alberta Machine Intelligence Institute (Amii), with research focusing on the intersection of software engineering and artificial intelligence. His academic journey includes a Ph.D. from Singapore Management University, an M.Sc. in Software System Engineering from University College London, and undergraduate studies at Yangzhou University. His research interests span Software Engineering, Artificial Intelligence, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models, and Cybersecurity. Yang's work explores how human and AI collaboration can improve code writing, how AI impacts open-source communities, and how developers build software in emerging environments like VR/AR. His recent publications demonstrate strong trends in code language models, with significant contributions to ASE, ICSE, ISSTA, and top journals like TOSEM and TSE. His research addresses critical challenges including token efficiency in code generation, user perception of AI coding assistants, privacy preservation in code models, and fairness in AI systems. 2024 IEEE Computer Society Best Paper Award (1 out of 183 submissions) ACM Distinguished Paper Award from ISSTA 2024 Distinguished Reviewer Award from Internetware 2024 SMU Research Staff Excellence Award (1 of 4 university-wide) SMU Presidential Fellowship Award SMU Dean's List Award 1st Place in ACM Student Research Competition at ICSE 2024 Yang actively mentors graduate students with regular one-on-one meetings, constructive feedback, and support for top-venue publications. He provides full funding through teaching assistantships and research grants, including travel support for conferences. His lab focuses on responsible research conduct and societal implications of AI work. Though early-career, he actively builds professional networks for students through collaborations and supports diverse career paths in academia and industry. He leads research in the Alberta Machine Intelligence Institute, focusing on practical applications where software engineering principles enhance AI systems and where AI techniques solve real-world software engineering challenges.

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Xiao YuView profile

Researcher

Max Planck Institute for Security and PrivacyGermany

Xiao Yu is a Research Fellow (Assistant Research Professor) at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Previously, they were a Postdoctoral Researcher at Huawei under Prof. Xin Xia. They hold dual PhD degrees: from Wuhan University's School of Computer Science (December 2020) supervised by Prof. Jin Liu, and from City University of Hong Kong's Department of Computer Science (March 2021) supervised by Prof. Qing Li and Prof. Jacky Wai Keung. Research focuses on three interconnected domains: LLMs Data Governance and Evaluation addressing hallucination phenomena and task-specific LLM evaluation in software engineering; Intelligent Software Engineering leveraging deep learning for code generation, annotation, and maintenance; and Software Security and Reliability investigating vulnerability detection, log anomaly identification, and security bug classification. Their work bridges theoretical advancements with industrial applications, particularly in blockchain and data security contexts. Recent publications demonstrate strong trends in realistic LLM evaluation (RealisticCodeBench), vulnerability detection using semi-supervised learning, and industrial anomaly detection. Key thematic areas include effort-aware defect prediction, code smell detection, and the practical application of large language models in software engineering tasks, with increasing emphasis on data quality and privacy considerations. Xiao Yu actively contributes to the academic community through extensive service roles including journal reviewing for ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Dependable and Secure Computing, and serving on program committees for major conferences like APSEC 2025 and ASE 2025. They have supervised numerous graduate students as evidenced by authorship patterns in publications. Based at Zhejiang University's State Key Laboratory of Blockchain and Data Security, their research operates at the intersection of academic rigor and industrial relevance, with strong collaborations spanning multiple institutions including Huawei, Wuhan University, and City University of Hong Kong.

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Xiaoning DuView profile

Senior Lecturer

Max Planck Institute for Security and PrivacyGermany

Xiaoning Du is a Senior Lecturer (equivalent to U.S. Associate Professor) at the Department of Software Systems and Cybersecurity within the Faculty of Information Technology at Monash University, Australia. She was promoted to this position effective July 1, 2025, having previously served as a Lecturer (Assistant Professor) since joining Monash in February 2021. Her research bridges the gap between theory and practical applications of program analysis and formal methods in evaluating traditional and AI-assisted software systems. Dr. Du's educational background includes: PhD from Nanyang Technological University (2020) Bachelor's degree from Fudan University (2014) Dr. Du specializes in software engineering, artificial intelligence, and cybersecurity , with particular focus on SE4AI (Software Engineering for AI), software analysis and testing . Her research has made significant contributions to the security and quality assurance of intelligent software systems, especially intelligent software engineering tools. She is best known for her work on Devign , BigCodeBench , DeepStellar , and SimPy , which have advanced the fields of code generation, program analysis, and AI security. Her approach consistently bridges theoretical foundations with practical applications to improve software quality and security. Dr. Du's recent publications demonstrate a strong focus on the intersection of software engineering and AI, particularly examining how large language models interact with source code. Her work addresses critical challenges in code generation efficiency, security vulnerabilities in AI-assisted development, and fairness in AI systems. She has made significant contributions to benchmarking frameworks like BigCodeBench and has pioneered research on watermarking techniques to protect code datasets from misuse by neural code completion models. Dr. Du has received numerous prestigious awards and recognitions: 2024 Google Research Scholar Award in Software Engineering ACM SIGSOFT Distinguished Paper Award (ISSTA'24) ICLR Oral presentation (2025) 2024 FIT Dean's Early Career Researcher of the Year Award Multiple FIT ECR Seed Grants (2021-2023) Dr. Du actively mentors PhD students, with notable successes including Terry (2024-2025 IBM PhD Fellowship Award recipient) and Zhensu (2024 Bytedance Scholarship Award recipient). She is currently seeking self-motivated PhD students with strong programming skills and relevant research experience, offering full scholarship support. Her research has been supported by multiple grants including the Google Research Scholar Program award and several FIT ECR Seed Grants that have enabled her team to pursue innovative research in software security and AI-assisted development. Dr. Du leads a research group focused on intelligent software systems security and quality assurance. Her team has developed several influential tools and benchmarks including Devign, BigCodeBench, DeepStellar, and SimPy. These resources have become important assets for researchers and practitioners working at the intersection of software engineering and artificial intelligence, particularly in the areas of code generation, program analysis, and security testing of AI systems.

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Pinjia HeView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.

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Zhiyuan WanView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Zhiyuan Wan is an Associate Professor in the College of Computer Science and Technology at Zhejiang University, China. His academic career spans multiple prestigious institutions across North America and Asia, with a focus on advancing software engineering practices through empirical research and tool development. Dr. Wan's educational background includes: Ph.D. in Computer Science from Zhejiang University (2014) His postdoctoral journey featured positions at: University of British Columbia, Canada (2019-2020) Singapore Management University (2018) Zhejiang University (2016-2020) Lehigh University, United States (2014-2015) Dr. Wan's research program centers on empirical software engineering with particular expertise in blockchain technologies and software security. His work bridges theoretical insights with practical tool development, focusing on: Smart contract security and vulnerabilities in cryptocurrency ecosystems Code search and recommendation systems for developer productivity Empirical studies of developer practices and challenges Impact of machine learning on software development workflows His approach combines rigorous empirical methods with practical tool building to address real-world challenges faced by software practitioners. Analysis of Dr. Wan's recent publications reveals a strategic evolution toward blockchain security research, beginning around 2020 with studies on smart contract security and expanding to cover NFT ecosystems, Solana blockchain transactions, and cross-chain vulnerabilities. His work consistently applies empirical methods to uncover practical insights while developing tools that directly address identified challenges in software development. Dr. Wan actively contributes to the software engineering community through service on program committees for major conferences including ASE, ICSE, ESEC/FSE, and ISSTA. His academic leadership extends to mentoring relationships with students and collaborators across international institutions, though specific advisees are not documented in the provided materials.

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Sven ApelView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Professor Sven Apel holds the Chair of Software Engineering at Saarland University's Saarland Informatics Campus in Germany. He is also the Director of the Saarbrücken Graduate School of Computer Science. His work focuses on software engineering with an emphasis on automation, human factors, and interdisciplinary approaches. Prof. Apel received his Ph.D. in Computer Science in 2007 from the University of Magdeburg. His academic journey includes: Ph.D. in Computer Science, University of Magdeburg (2007) Emmy-Noether Fellowship of the German Research Foundation Heisenberg Professorship of the German Research Foundation Prof. Apel's research centers on empowering software engineering practice to enter an era of intensive automation. His key research areas include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and empirical and neurophysiological methods. He pays special attention to the human factor and interdisciplinary research questions, applying his findings to real-world software systems from both open-source projects and industry collaborations with partners like Siemens AG, Bosch Engineering, and Airbus Helicopters. Analysis of Prof. Apel's recent publications reveals a strong focus on configurable software systems, neurophysiological approaches to understanding programming, and the application of AI techniques to software engineering problems. His work often bridges the gap between theoretical foundations and practical applications, with many studies involving industrial collaborations. There's a noticeable trend toward interdisciplinary research combining software engineering with neuroscience, organizational studies, and machine learning. Prof. Apel has received numerous prestigious awards and honors: ERC Advanced Grant "Brains On Code" (2022) ACM Distinguished Member for "Outstanding Scientific Contributions to Computing" (2018) Multiple Most Influential Paper Awards (SPLC'18, ICPC'22, GPCE'23) Multiple Best Paper Awards (SPLC'11, Modularity'15, AOM'18) Heisenberg Professorship and Emmy-Noether Fellowship from the German Research Foundation Prof. Apel has advised numerous Ph.D., Master's, and Bachelor's students throughout his career. His research has been generously funded by multiple grants including an ERC Advanced Grant (2,500,000 Euro, 2022-2027), several DFG projects (CPEC, Congruence, Pervolution), and previous grants like SafeSPL, FeatureFoundation, and Pythia. His work has practical impact through collaborations with industry partners including Siemens AG, Bosch Engineering, and Airbus Helicopters. Prof. Apel leads research in the Chair of Software Engineering at Saarland University, where his team explores the intersection of software engineering, neuroscience, and artificial intelligence. His "Brains On Code" ERC project specifically investigates how programmers' brains process code using neuroimaging techniques. The research group maintains strong connections with both academic and industry partners, facilitating the transfer of research findings into practical applications.

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Huaming ChenView profile

Senior Lecturer

Max Planck Institute for Security and PrivacyGermany

Dr. Huaming Chen is a Senior Lecturer in the School of Electrical and Computer Engineering at The University of Sydney, Australia. His work focuses on trustworthy machine learning systems, software engineering, and software security. With numerous publications in top-tier conferences and journals, Dr. Chen has established himself as a significant contributor to the fields of AI security and software engineering. Dr. Chen's primary research interests lie at the intersection of software engineering and artificial intelligence, with a strong emphasis on trustworthy AI systems. His work spans several key areas including: Software Security for AI-enabled systems Trustworthy and Responsible AI development Computational biology applications Industrial 4.0 implementations Federated learning and privacy-preserving techniques Large language model verification and uncertainty analysis His research addresses critical challenges in ensuring AI systems are secure, reliable, and ethically sound. Dr. Chen's recent publications demonstrate a strong trend toward addressing security and trustworthiness challenges in AI systems. His work spans multiple domains including software security (particularly for AI systems), trustworthy AI development, and applications in computational biology. A significant portion of his recent work focuses on large language models, examining their vulnerabilities, verification methods, and uncertainty analysis. He also maintains active research in federated learning, adversarial machine learning, and software security techniques. Dr. Chen has received several notable awards and recognitions: 2020 IEEE CIS Student Grant for IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2017 Student and Early Career Travel Fellowship for The 16th International Conference on Bioinformatics (InCoB 2017) 2017 Student Travel Award for 2017 IEEE World Congress on Services Dr. Chen actively supervises multiple research students working on cutting-edge projects related to trustworthy AI and software security. His current students are exploring topics ranging from blockchain-based governance frameworks to open-source AI security and digital twin platforms. He also serves in numerous committee roles at top conferences including area chair for ACM MM, and PC member for ACM CCS, IJCAI, KDD, and many others. His service as a Guest Editor for journals like Computers & Security and as a Grant Reviewer for UKRI demonstrates his standing in the research community. Dr. Chen organizes workshops focused on Trustworthy and Responsible AI, reflecting his commitment to advancing the field. His research group appears to focus on practical applications of AI security techniques, with projects spanning multiple domains including healthcare, finance, and industrial systems. He maintains active collaborations with researchers across multiple institutions, as evidenced by his co-authorship on diverse publications.

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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.

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Alexander SerebrenikView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Alexander Serebrenik is a full professor of social software engineering at the Eindhoven University of Technology in the Netherlands, working within the Department of Mathematics and Computer Science. His academic profile spans decades of interdisciplinary research bridging computer science and social sciences. Professor Serebrenik's research focuses on facilitating software evolution through understanding social aspects of development. His work integrates computer science methods (socio-technical coordination theory, natural language processing, machine learning) with organizational psychology principles. A consistent theme across his publications is empiricism - addressing software engineering challenges through observation and experimentation while balancing social and technical perspectives. His recent work increasingly emphasizes diversity, equity, and inclusion in software engineering, culminating in his 2024 book "Equity, Diversity, and Inclusion in Software Engineering: Best Practices and Insights" (APress). His publication record reveals evolving interests from socio-technical coordination to human factors, community dynamics in open source, and now DEI-focused research. Distinguished Paper Award at ICSE 2023 Distinguished Paper Award at MSR 2023 Distinguished Reviewer Award at FSE 2020 Senior member of IEEE Member of ACM As an academic leader, Professor Serebrenik has mentored PhD students including Tukaram Muske, and actively participates in doctoral symposia. His service includes roles as Diversity and Inclusion Co-Chair at multiple conferences and extensive program committee participation across the software engineering conference landscape. His research group at TU/e maintains an active program studying the social dimensions of software engineering through empirical investigations, contributing significantly to understanding how human factors influence development processes and outcomes.

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Djamel Eddine KhelladiView profile

Researcher

Max Planck Institute for Security and PrivacyGermany

Djamel Eddine Khelladi is a CNRS Researcher at the IRISA laboratory within the DIVERSE team at University of Rennes, specializing in software engineering with emphasis on model-driven techniques and empirical validation. His work bridges theoretical frameworks and industrial-scale applications, particularly in evolving software ecosystems. His academic foundation includes a Ph.D. from Sorbonne University (formerly University Pierre et Marie Curie) at the Laboratory of Computer Science of Paris 6 (LIP6), followed by postdoctoral research at Johannes Kepler University Linz's Institute for Software Systems Engineering. This trajectory established his expertise in software evolution and model-driven approaches. Khelladi's research centers on software evolution challenges, particularly model-code co-evolution in highly-configurable systems like the Linux kernel. He develops scalable analysis tools (e.g., HyperAST, HyperDiff) and investigates empirical phenomena in build systems, configuration management, and polyglot programming environments. Recent work increasingly integrates large language models for automated co-evolution tasks while maintaining rigorous empirical validation. His publication trends reveal a consistent focus on practical tooling for software evolution, with growing exploration of AI-assisted engineering. Key themes include scalability in software history analysis, reproducibility in configurable systems, and debugging multi-language environments, often using Linux kernel ecosystems as testbeds. As an active community contributor, Khelladi serves on program committees for ASE, ICSE, and ESEC/FSE while advancing research through the DIVERSE team at IRISA. This group specializes in variability-intensive software systems, providing the collaborative environment for his empirical and tool-building research.

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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.

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