Code Readability Professors and Supervisors in Germany | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱-۱۵ از ۳۴ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Tien N. Nguyenمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.

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Sarah Fakhouryمشاهده پروفایل

پژوهشگر

Max Planck Institute for Security and PrivacyGermany

Sarah Fakhoury is a Senior Researcher in the Research in Software Engineering (RiSE) group at Microsoft Research, Redmond. Her work bridges formal methods, empirical software engineering, machine learning, and human-computer interaction to optimize developer cognitive effort in AI-assisted programming tools. Her research focuses on trustworthy AI for code generation , leveraging formal verification to ensure correctness in LLM-generated outputs. Key areas include program comprehension, source code readability, and empirical evaluation of developer-AI interaction. She develops tools like 3DGen for provably correct binary parsers and NL2Fix for natural language-based code repair. Her publications reveal strong trends in formal methods integration with AI (60% of recent work), empirical developer studies (30%), and readability/metrics innovation (10%). Keywords cluster around program verification, LLM evaluation, and cognitive load measurement. ACM/SIGSOFT Distinguished Paper Award (ICPC 2018) Fakhoury actively contributes to the academic community as PC member for ASE, ICSE, and ESEC/FSE. She co-organizes workshops like Muslims in ML at NeurIPS and mentors through SMeW. Her RiSE group collaboration with Shuvendu Lahiri and Madanlal Musuvathi drives Microsoft's trustworthy AI4Code initiatives, focusing on verifiable developer tools.

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David Loمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.

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Gregory Gayمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Gregory Gay is an Associate Professor in the Interaction Design and Software Engineering division within the Department of Computer Science and Engineering at Chalmers University of Technology and the University of Gothenburg, Sweden. His academic profile spans numerous software engineering conferences where he has served as committee member, program chair, and active researcher since at least 2018. Dr. Gay's research focuses on the intersection of software engineering and artificial intelligence, with particular emphasis on: Software Testing and Analysis Search-Based Software Engineering AI for Software Engineering (AI4SE) AI Engineering Automation of development tasks Software Carbon Footprint and sustainability His recent publications demonstrate a strong trend toward applying AI and optimization techniques to software testing challenges, with increasing focus on sustainability aspects of software development. Many studies take an industrial perspective, examining real-world applications in automotive software systems. His work blends theoretical foundations with practical applications, making significant contributions to both academic research and industrial practice in software engineering. Dr. Gay has been actively involved in numerous top software engineering conferences including ASE, ICSE, ESEC/FSE, ISSTA, and ICST, serving on program committees and organizing tracks. His research methodology typically combines optimization, artificial intelligence, and machine learning to help developers deliver complex systems in a safe, secure, and efficient manner.

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Elena Spörerمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Elena Spörer is a researcher at the Chair of Computer Science Education at Technische Universität München (TUM) . Her work focuses on data literacy, debugging processes, code quality in K-12 education, and pedagogical approaches to quantum computing. She contributes to curriculum development and teacher training initiatives.

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Sandro Schulzeمشاهده پروفایل

استاد

Anhalt University of Applied SciencesGermany

Prof. Dr. Sandro Schulze is a Professor in the Department of Computer Science and Languages at Anhalt University of Applied Sciences. His research focuses on software engineering, particularly in areas such as software architecture analysis, software product line engineering, and variability management. He leads efforts in developing tools for visualizing fork ecosystems and tracking architecture smells. His work bridges theoretical research with practical applications in open-source systems and industrial software. Responsibilities include overseeing the Software Engineering research group within the department. He is actively involved in academic activities, including organizing workshops like WSRE and contributing to initiatives like the Software Reengineering Body of Knowledge (SREBOK). Publications highlight advancements in fork ecosystem visualization (e.g., VisFork toolsuite), empirical studies on architecture smells, and techniques for feature extraction from requirements. His work often emphasizes tool development and empirical validation of software engineering practices.

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Jan Trosienمشاهده پروفایل

پژوهشگر

University of HamburgGermany

Jan Trosien is a Doctoral Researcher in Ancient History at the University of Hamburg's Centre for the Study of Manuscript Cultures (CSMC). His research focuses on inscriptions containing legal regulations in Ancient Athens, examining their material qualities and communicative roles within the polis. He is part of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA) under Research Field B07. Education: Master of Arts (MA) Research Interests: Trosien investigates how legal inscriptions in Athens functioned as written artefacts, analyzing their visibility, readability, and comprehensibility. His work challenges traditional views on the symbolic vs. functional roles of inscribed laws. Projects: His dissertation project, 'Communicating a Code of Conduct,' explores the intersection of procedural law and materiality in Athenian inscriptions. He is affiliated with UWA, a multidisciplinary initiative studying manuscript cultures. Contact: Trosien can be reached at his email jan.trosien@uni-hamburg.de or via the CSMC office in Hamburg.

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Rocco Olivetoمشاهده پروفایل

استاد

University of TrierGermany

Rocco Oliveto is a prominent researcher in software engineering with extensive contributions across multiple domains including code quality assessment, smart contracts, Docker configuration analysis, and healthcare applications of AI. His collaborative work spans numerous institutions, with frequent co-authorship with researchers such as Simone Scalabrino, Gabriele Bavota, and Emanuela Guglielmi. Dr. Oliveto's research interests focus on practical software engineering challenges with emphasis on code readability, API compatibility, bug prediction, and smart contract maintenance. His work bridges theoretical research with practical applications, particularly evident in recent projects applying machine learning to healthcare diagnostics and video game quality analysis. His research demonstrates a consistent trajectory toward addressing real-world software engineering problems with innovative methodological approaches. Analysis of his recent publications reveals a strong trend toward interdisciplinary research, particularly at the intersection of software engineering and healthcare applications. His work shows increasing focus on practical applications of AI in medical diagnostics, rehabilitation technology, and patient monitoring systems, while maintaining strong contributions to core software engineering topics like code quality and developer productivity. The diversity of publication venues—from top software engineering journals like Empirical Software Engineering and ACM TOSEM to healthcare conferences like BIOSTEC—demonstrates the breadth of his research impact. Dr. Oliveto has demonstrated significant research leadership through numerous collaborative projects, particularly evident in his participation in the QualAI project focused on continuous quality improvement of AI-based systems. His work shows consistent funding support through collaborative research initiatives that bridge academic and practical software engineering concerns.

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Prem Devanbuمشاهده پروفایل

استاد پژوهشی

Max Planck Institute for Security and PrivacyGermany

Prem Devanbu is a Research Professor of Computer Science at the University of California, Davis, where he has been a faculty member since transitioning from his industrial R&D position at Bell Labs in New Jersey. He holds a distinguished position in the Department of Computer Science within the College of Engineering, focusing on cutting-edge research at the intersection of software engineering and artificial intelligence. Dr. Devanbu earned his B.Tech from the Indian Institute of Technology (IIT) Madras and completed his Ph.D at Rutgers University under the supervision of Alex Borgida. His career path from industry to academia has shaped his practical yet research-oriented approach to software engineering problems. Devanbu's research primarily centers on Empirical Software Engineering , the Naturalness of Software , and Software Engineering education . His groundbreaking work on the naturalness hypothesis—that software exhibits statistical properties similar to natural language—has profoundly influenced the field. This research has expanded to explore bimodality in software (its dual nature as both machine-executable code and human-readable text), opening new avenues for analysis and tool development. His recent work heavily focuses on the application of Large Language Models to software engineering tasks, particularly in code summarization, program repair, and type inference. Analysis of Dr. Devanbu's recent publications reveals a clear trend toward leveraging Large Language Models for software engineering tasks. His research demonstrates how statistical properties of code can be exploited to improve software development processes, with particular emphasis on program understanding, documentation generation, and automated repair. The work bridges theoretical insights about code naturalness with practical applications that address real-world software maintenance challenges. Dr. Devanbu has received numerous prestigious awards recognizing his contributions to the field: ACM SIGSOFT Outstanding Research Award (2021) - "for profoundly changing the way researchers think about software by exploring connections between source code and natural language" Alexander von Humboldt Research Award (2022) IEEE Computer Society Harlan Mills Award (2024) ACM Fellow Six "test-of-time" or "10 year most influential paper" awards (MSR 2006, MSR 2009, ESEC/FSE 2008, ESEC/FSE 2009, ESEC/FSE 2011, ICSE 2012) Throughout his career, Dr. Devanbu has been actively involved in mentoring the next generation of software engineering researchers, serving on doctoral committees, and participating in New Faculty Symposia to support early-career academics. His research has been supported by significant grants that have enabled his team to explore innovative approaches at the intersection of empirical methods and software tool development. At UC Davis, he has contributed to building a strong software engineering research group that bridges theoretical insights with practical applications. Dr. Devanbu leads research efforts focused on understanding the statistical properties of software and leveraging these insights to build practical tools. His work on the naturalness and bimodality of code has established a framework that continues to influence how researchers approach program analysis and software development. His current team is at the forefront of exploring how Large Language Models can be effectively applied to software engineering tasks while accounting for the unique characteristics of code as a specialized form of human communication.

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Fang Liuمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Software Engineering
Artificial Intelligence for Software Engineering
Program Understanding
+۳ مورد دیگر

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

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Marco Munderlohمشاهده پروفایل

پژوهشگر ارشد

Leibniz University HannoverGermany

Marco Munderloh is a research fellow at the Leibniz University Hannover under the Institute for Information Processing . His career spans advanced video coding, predictive maintenance, and photonic design, with a focus on low-bitrate aerial video compression and motion detection. Dipl.-Ing. in Computer Engineering (2004), Technical University of Ilmenau PhD (2015), Leibniz University Hannover His research interests include: Wave Field Synthesis (WFS) with patented applications in cinema sound systems Predictive maintenance using deep learning and Bayesian neural networks Inverse design for photonic integrated circuits and 3D nanostructures Region-of-interest (ROI) video coding and motion compensation Optical verification of construction materials Marco's recent publications (2025–2018) cover predictive maintenance, video compression, and photonic design. Key trends involve applying machine learning to industrial reliability, optimizing video codecs for aerial surveillance, and computational inverse design for optical devices. Scientific achievements include: Patent for WFS technology in sound reproduction Collaborations span institutions like IEEE, CIRP, EUSPEN, and journals such as APSIPA Transactions and Bautechnik . Contact: Marco.Munderloh@tnt.uni-hannover.de , Marco.Munderloh@web.de .

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Tomoki Nakamaruمشاهده پروفایل

استادیار

Max Planck Institute for Software SystemsGermany

Tomoki Nakamaru is an Assistant Professor at the Department of Multidisciplinary Systems Science, Graduate School of Arts and Sciences, The University of Tokyo. He holds a Ph.D. in Information Science and Engineering from The University of Tokyo, advised by Prof. Shigeru Chiba. Education: PhD (2021), The University of Tokyo M.Sc. (2018), The University of Tokyo B.Eng. (2016), The University of Tokyo His research focuses on Domain-Specific Languages , Mining Software Repositories , and Notebook Programming . He develops tools like Multiverse Notebook and Silverchain to enhance code exploration and API usability. He has published extensively at top venues like OOPSLA and PLDI , covering topics from Fluent Interface Generation to Runtime Monitoring . His 15 most recent works span 2017–2025. Scientific Awards: Yamashita Memorial Research Award (2023) University of Tokyo Business Reform Special Award (2021) JSST Student Encouragement Awards (2019–2020) Distinguished Artifact Award at OOPSLA (2019) Dean's Award, UTokyo (2018) Nakamaru serves on program and artifact committees for conferences like ECOOP , OOPSLA , and ‹Programming› . He teaches courses including Practical Programming and Introduction to Information Systems .

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Emanuel Regnathمشاهده پروفایل

مربی

Technical University of MunichGermany

Emanuel Regnath is a Tutor at the Chair of Embedded Systems and Internet of Things under Prof. Dr. Sebastian Steinhorst at the Technische Universität München . His work spans research, teaching, and supervision of student theses. Research focuses on Secure Decentralization Methodologies for the Internet of Things Teaching includes courses like Software Architecture for Distributed Embedded Systems and IoT Security His research interests include: Consensus Protocols for Cyber-Physical Systems synchronization Blockchain applications in resource-constrained devices Lightweight Message Authentication using hash-based signatures Key publication trends span: Decentralized vehicle coordination (2019-2021) Blockchain verification for IoT (2015-2020) Security architectures for autonomous systems (2017-2021) Supervised student work includes: Tariro Chingosho: "Designing a Notation System for Consensus Algorithms" (2021) Donát Nagy: "Reputation Framework to Increase Reputation Accuracy" (2021) Markus Moskopp: "Implementation and Evaluation of Consensus Protocols for IoT Systems" (2021) ...and 12 other theses from 2015-2021.

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Stefan Fuchsمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Dr. Stefan Fuchs is a researcher at the Technical University of Munich (TUM), working in the Chair of Computing in Civil and Building Engineering within the TUM School of Engineering and Design. His research focuses on applying artificial intelligence techniques to challenges in the built environment, particularly in the areas of building regulations and compliance checking. Dr. Fuchs completed his doctoral dissertation in 2024 titled "Transformer-based Semantic Parsing of Building Regulations: Supporting Regulators in Drafting Machine-Interpretable Rules" at ResearchSpace@Auckland. His work bridges the gap between natural language processing and construction engineering, developing novel approaches to interpret and represent building regulations in machine-readable formats. His primary research interests include Natural Language Processing for building regulations, Automated Compliance Checking systems, Knowledge Representation and Reasoning techniques, and AI applications in the Built Environment. He has pioneered approaches using transformer models and intermediate representations to improve the semantic parsing of building regulations, enabling more effective automated compliance checking systems. His research spans both theoretical frameworks and practical tool development, including a LegalRuleML editor with transformer-based autocompletion. Dr. Fuchs' publication record demonstrates consistent focus on transforming natural language building regulations into machine-interpretable formats. His work shows progression from foundational literature reviews to sophisticated transformer-based approaches, with recent publications exploring parallel drafting processes and large language model applications. This research trajectory indicates growing sophistication in addressing the complex challenges of regulatory compliance in construction. He actively contributes to the academic community through teaching "Artificial Intelligence in Engineering" and maintains a strong research presence with profiles on GitHub, LinkedIn, ORCID, Google Scholar, and ResearchGate. His GitHub profile (stefan-1992) shows additional contributions to machine learning projects beyond his primary research focus, including work on "grokfast" related to accelerating machine learning phenomena. Dr. Fuchs collaborates extensively with researchers including Prof. André Borrmann, Judith Fauth, and Robert Amor. His work is situated within TUM's broader research initiatives in Building Information Modeling, Digital Twinning, and Knowledge Representation and Reasoning, contributing to the university's leadership in construction informatics.

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Yanlin Wangمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Yanlin Wang is an Assistant Professor at the School of Software Engineering, Sun Yat-sen University, where he joined in July 2022 after working as a senior researcher at Microsoft Research Asia in the Data, Knowledge, and Intelligence (DKI) group. His academic journey includes a B.S. from Zhejiang University and a Ph.D. from the University of Hong Kong under the supervision of Prof. Bruno C. d. S. Oliveira. Dr. Wang's research spans multiple domains within computer science, with particular emphasis on applying large language models to software engineering problems. His work bridges traditional software engineering with cutting-edge AI techniques, focusing on code generation, code search, and intelligent software development tools. He has made significant contributions to understanding how LLMs can be effectively applied to repository-level code completion, security analysis of smart contracts, and improving the efficiency of code generation processes. An analysis of his recent publications reveals a strong trend toward applying AI to practical software engineering challenges, particularly in the areas of code generation quality, security analysis, and efficient model deployment. His work often combines theoretical insights with practical implementations, as evidenced by multiple tools and frameworks developed by his research group, including MemoryBank for enhancing LLMs with long-term memory and AGIEval for evaluating foundation models. ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) - for work on LLM code generation acceleration ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) - for work on identifying code security issues ACM SIGSOFT Distinguished Paper Award (Internetware 2024) Distinguished Reviewer Award (Internetware 2024) Best Paper Award (Modularity 2016) Dr. Wang actively contributes to the academic community through service on program committees for major conferences including ISSTA, FSE, ICSE, and COLING. He serves as a reviewer for prestigious journals and conferences such as TKDE, TNNLS, and AAAI. His teaching portfolio at Sun Yat-sen University includes Principles of Compilers, AI Revolution: Decoding Large Models, and Software Source Codes Analysis, reflecting his expertise across both foundational and cutting-edge topics in computer science.

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