Code Clone Analysis Professors and Supervisors

Eunjong Choiمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Eunjong Choi is an Associate Professor at the Faculty of Information and Human Sciences, Kyoto Institute of Technology in Japan. She joined Kyoto Institute of Technology as a tenure-track assistant professor in 2019 and was promoted to associate professor in March 2024. Prior to this position, she served as an assistant professor at Nara Institute of Science and Technology (April 2016-March 2019) and at Osaka School of International Public Policy, Osaka University (April 2015-March 2016). Kyoto Institute of Technology (2019-Present) Nara Institute of Science and Technology (2016-2019) Osaka University (2015-2016) Dr. Choi's research focuses on software engineering with particular emphasis on software maintenance and evolution, program comprehension, software analytics, reused code management and detection, refactoring support, and test code generation. Her work bridges theoretical software engineering concepts with practical applications in industrial settings. She has investigated topics such as token-level SZZ algorithms, deep learning-based clone detectors, and modernization of large-scale industrial systems. Dr. Choi has held significant leadership roles in major software engineering conferences including serving as General Chair for ICPC 2020 and IWESEP 2017, Publicity Co-Chair for ICSE 2020, and Local Arrangement Co-Chair for SANER 2016. She has been actively involved in the IEEE Kansai Section Woman in Engineering Affinity Group, serving as Secretary (2016-2017) and Vice-Chair (2018-2021). Her teaching portfolio includes Software Mining and Analysis, Software Metrics, and Software Engineering courses at Kyoto Institute of Technology; Software Design and related courses at Nara Institute of Science and Technology; and Basic Information Literacy at Osaka University. Dr. Choi earned her Ph.D. in Information Science and Technology from Osaka University in 2015 and maintains an active research profile in the software engineering community.

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Eric Boddenمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Eric Bodden is a Professor for Secure Software Engineering at Paderborn University and co-director of Fraunhofer IEM. He is also a member of the directorate of the Collaborative Research Center CROSSING at TU Darmstadt. As an ERC fellow, Bodden leads the Attract-Group on Secure Software Engineering at Fraunhofer IEM, where he develops code analysis technology for security in collaboration with leading national and international software development companies. Bodden is one of the leading experts in secure software engineering with a specialty in building highly precise tools for automated program analysis. His research spans static program analysis, Android security, taint analysis, and cryptographic API security. He has made significant contributions to the field through frameworks like FlowDroid for Android analysis, DroidBench benchmark suite, and as one of the chief maintainers of the Soot program analysis framework. His work bridges theoretical advances with practical applications, focusing on creating tools that can be effectively used by developers to enhance software security. His recent publications demonstrate a continued focus on advancing static analysis techniques, with increasing attention to modern challenges like C/C++ analysis, large language models in program analysis, and addressing security vulnerabilities in dependency management. His work shows a consistent trajectory of improving analysis precision, scalability, and usability for developers. Heinz Maier-Leibnitz-Preis (2014) ERC fellow BITKOM Management Club membership (2013) Bodden has advised numerous doctoral students whose theses cover diverse aspects of secure software engineering, including static analysis for Android applications, secure integration of cryptographic software, and information flow security engineering. His research group has received recognition through multiple dissertation awards including Summa cum laude distinctions and the Ernst Denert Software Engineering Award. The group has developed influential tools like FlowDroid, CogniCrypt, and SootUp that have shaped the field of secure software development. At Fraunhofer IEM, Bodden heads the Attract-Group on Secure Software Engineering, which collaborates with industry partners to develop practical security analysis solutions. His team has created several influential frameworks including FlowDroid for Android security analysis and the DroidBench benchmark suite. The group's work bridges academic research with real-world applications, focusing on making security analysis tools more precise, scalable, and usable for developers.

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Istvan Davidمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Istvan David is an Assistant Professor of Software Engineering at McMaster University's Faculty of Engineering, Department of Computing and Software, where he leads the Sustainable Systems and Methods Lab (SSM) and works as a researcher in the McMaster Centre for Software Certification (McSCert). His work bridges the gap between model-driven engineering, digital twins, and sustainability in systems engineering. Dr. David's research interests focus on digital twins , model-driven engineering , sustainability in computing, cyber-physical systems , and collaborative modeling . His lab develops novel reinforcement learning techniques , digital twin architectures , and modeling and simulation methods with a strong emphasis on sustainability as both a system characteristic and an engineering principle. His recent publications reveal a strong trend toward integrating digital twin technology with sustainability concerns, particularly in automotive systems, smart farming, and resource-efficient software engineering. The research spans from foundational modeling techniques to applied solutions addressing the four essential sustainability dimensions of technical systems: technical (long-term usage), economic (financial viability), environmental (reduced impact), and social (elevated utility). Scientific Awards: Best Practice Paper Award at MODELS Best Paper 2024 Runner-up in the Journal of Computer Languages Dr. David actively mentors students including PhD candidate Xiaoran (Sharon) Liu, and has supervised undergraduate researchers like Adwita Kashyap and Kyanna Dagenais. He has secured multiple research grants supporting work on sustainable systems and digital twin technologies, with applications in automotive engineering, smart farming, and resource-efficient computing. His lab collaborates with industry partners and academic institutions worldwide, including VU Amsterdam and University of Montréal. The Sustainable Systems and Methods Lab focuses on the vision of 'sustainable systems by sustainable methods,' addressing the growing problem of unsustainable systems engineering practices. The lab's work on bipartite sustainability aims to both build sustainable systems and develop systems using sustainable methods.

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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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Michael X. Zhuمشاهده پروفایل

استاد

The University of Texas Health Science Center at HoustonUnited States

Dr. Michael X. Zhu is a Professor at The University of Texas Health Science Center at Houston with extensive expertise in ion channel research and calcium signaling. His academic background includes a Ph.D. from the University of Houston (1991), an M.S. from the University of Houston (1988), and postdoctoral training at Baylor College of Medicine (1994). Dr. Zhu's research focuses on the molecular mechanisms of ion channels, particularly Transient Receptor Potential (TRP) channels and two-pore channels (TPCs). His work has significantly advanced understanding of calcium signaling pathways in cellular physiology. He has made seminal contributions to the field, including the cloning and characterization of TRPC channels and the identification of TPCs as NAADP receptors in endolysosomal membranes. Analysis of Dr. Zhu's recent publications (2020-2022) reveals a strong focus on calcium signaling mechanisms, with particular emphasis on TRP channels and their roles in neuronal function, pain sensation, and disease processes. His work spans multiple disciplines including neuroscience, molecular pharmacology, and cell biology, demonstrating an integrative approach to understanding complex cellular signaling pathways. Dr. Zhu's research program addresses fundamental questions in cellular physiology with implications for understanding and treating various diseases, including neurological disorders, pain conditions, and metabolic diseases. His laboratory combines molecular, pharmacological, and physiological techniques to elucidate the mechanisms of ion channel function and regulation.

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Samantha Elaine Dayمشاهده پروفایل

استادیار

Midwestern UniversityUnited States

Samantha Elaine Day is an Assistant Professor in the Department of Biochemistry and Molecular Genetics at Midwestern University's College of Graduate Studies in Arizona. She joined the university in 2022 after completing her postdoctoral training at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Education: Ph.D. in Biology from Arizona State University (2017) Dr. Day's research focuses on the intersection of epigenetics, particularly DNA methylation, and metabolic disorders such as type 2 diabetes and obesity. Her work investigates how environmental factors influence gene regulation through epigenetic mechanisms, with special attention to how these changes relate to disease progression. The Day Lab specifically examines DNA methylation patterns in human blood to identify biomarkers for diabetic kidney disease and other complications of type 2 diabetes. Her research combines biochemical, genetic, and epigenetic techniques to characterize altered methylation sites and their functional consequences. Dr. Day's publication record demonstrates consistent contributions to understanding the genetic and epigenetic basis of obesity, insulin resistance, and type 2 diabetes, with particular focus on American Indian populations. Her work employs advanced techniques including next-generation sequencing, methylation profiling, and functional validation of genetic variants. As an educator, Dr. Day teaches multiple biochemistry courses across various programs at Midwestern University, including Biochemistry I and II, Basic Integrated Sciences, and a Research Elective. Her teaching spans the Doctor of Osteopathic Medicine (D.O.), Doctor of Pharmacy (Pharm.D.), Doctor of Dental Medicine (D.M.D.), Doctor of Optometry (O.D.), and Doctor of Podiatric Medicine (D.P.M.) programs. The Day Lab conducts research on diabetic kidney disease, analyzing DNA methylation data from blood samples in relation to kidney parameters. The lab employs techniques such as PCR amplification, cloning, in vitro methylation, cell transfection, and luciferase reporter assays to validate the functional impact of identified methylation sites.

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Xin Xiaمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

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Zhiyuan Wanمشاهده پروفایل

دانشیار

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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Ted Zeruchaمشاهده پروفایل

استاد

Appalachian State UniversityUnited States

Ted Zerucha is a Professor in the Department of Biology at Appalachian State University and serves as Assistant Vice Provost for General and Experiential Education. His academic leadership extends beyond research into university-wide educational initiatives. His educational foundation includes: Ph.D. from the University of Ottawa Dr. Zerucha's research centers on gene and genome evolution within embryonic development, with specialized focus on Meis and Dlx homeobox gene families. Using zebrafish as a model organism, he investigates how gene expression patterns are regulated during development and how these mechanisms evolve following genome duplications. His work leverages zebrafish's unique evolutionary history—including an additional genome duplication event compared to mammals—to study gene retention and functional divergence in paralogous genes. His publication record (1997-2016) reveals consistent exploration of homeobox gene regulation, with recurring themes in transcriptional control mechanisms, evolutionary developmental biology, and comparative genomics between zebrafish and mammalian systems. Key research threads include enhancer function in forebrain development, cross-regulatory interactions between homeobox genes, and the impact of genome duplication on gene family evolution.

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Chaiyong Ragkhitwetsagulمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

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

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Hongyu Zhangمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

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

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