Code Smell Detection Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

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

نمایش ۴۶-۶۰ از ۱۸۸ استاد راهنما

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

Michael W. Godfreyمشاهده پروفایل

استاد

University of WaterlooCanada

Affiliations & Roles Michael W. Godfrey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo . He holds the David R. Cheriton Faculty Fellowship and has served as an associate director of Cornell's M.Eng. program. His roles include: General Chair for ICPC 2025 (IEEE Program Comprehension) Member of steering committees for ICSME, MSR, SCAM, and SWAN Course coordinator for CS138/CS246 and instructor for advanced topics courses Research Focuses on software evolution , program comprehension , and mining software repositories . His work addresses challenges in code clone analysis, developer productivity, and empirical software engineering. Notable contributions include: Advocating for intentional cloning as valid design practice Pioneering studies on code review quality and anomaly detection Developing tools like JavaDUCK (educational project) and mel (model extraction) Awards & Recognition Recipient of: Best Paper Awards at WCRE 2006, 2011, 2013 Most Influential Paper Award at SANER 2016 Outstanding Reviewer Awards (ICSME 2019/2020) Service & Outreach Active in: Program committee roles for ICSE, ICSM, MSR, and 30+ conferences University service: Undergraduate Recruitment Committee (2016–present) Industry collaborations with CWI (Amsterdam), Sun Microsystems, and automotive software teams

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Mircea Lunguمشاهده پروفایل

دانشیار

IT University of CopenhagenDenmark

Mircea Lungu is an Associate Professor in Software Engineering at the IT University of Copenhagen. His research focuses on software ecosystems, software architecture, technical debt management, and immersive technologies for software development. He actively contributes to academia through publications, projects, and teaching. Affiliation: IT University of Copenhagen Academic Rank: Associate Professor Research Interests: Mircea explores the intersection of software engineering and virtual reality through projects like Immersive Software Archaeology , which leverages 3D environments for legacy system analysis. His work also addresses educational paradigms in software engineering, emphasizing live systems and experiential learning. Project Highlights: He leads the HPL-PATHS initiative (2023–2026) on hyper-personalized language education and collaborates on the ISA project (2020–2024) for virtual reality-based software exploration. Publication Trends: Recent work combines virtual reality with software engineering challenges, including collaborative exploration tools, technical debt visualization, and immersive analytics. His academic output spans conference proceedings, journal articles, and systematic reviews. Contact: Email: mlun@itu.dk

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Eman AlOmarمشاهده پروفایل

استادیار

Stevens Institute of TechnologyUnited States

Eman AlOmar is an Assistant Professor in the Department of Systems and Enterprises at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2021). Her research focuses on the intersection of Software Engineering (SE) and Artificial Intelligence (AI), emphasizing software quality, refactoring, maintenance, technical debt, empirical studies, and mining software repositories. Education: PhD in Computing and Information Sciences (Rochester Institute of Technology, 2021). Research Interests: Software Maintenance & Evolution Empirical Software Engineering Mining Software Repositories Code Refactoring and Documentation Technical Debt Management AI Applications in Software Development Awards: 2024 Jess H. Davis Memorial Award for Research Excellence 2024 Best Mining Challenge Paper Award at MSR 2023 Distinguished Doctoral Research Award at MSR 2022 Best Reviewer Award at Journal of Systems and Software Grants: Co-PI on NSF DUE grant (2022-2025) to cultivate performance-aware software engineers, totaling $299,754. Teaching: Courses include Artificial Intelligence for Software Engineering, Software Testing & Quality Assurance, Object-oriented Software Engineering, and Informatics.

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Carolyn Seamanمشاهده پروفایل

استاد

University of Maryland, Baltimore CountyUnited States

Carolyn Seaman is a Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC), and Director of the Center for Women in Technology. Her research focuses on empirical studies of software engineering, particularly technical debt, software maintenance, and qualitative research methods. She also explores topics like communication in software teams and knowledge management. She holds affiliations with the Empirical and Applied Software Engineering Lab (EASEL) at UMBC, the International Software Engineering Research Network (ISERN), and serves on the editorial board of the Information and Software Technology Journal (ISTJ). Her work bridges theoretical and practical aspects of software development, emphasizing practitioner insights and organizational challenges. Dr. Seaman has mentored numerous PhD students, including graduates focused on software process improvement, open-source adoption, and technical debt management. Her current advisees include Brian Frey and Abdullah Aldaeej. Her publications span journals like IEEE Transactions on Software Engineering and conferences such as the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. Her research trends highlight technical debt management, qualitative methods in software engineering, and the intersection of software practices with organizational structure. Awards are not explicitly mentioned, but her contributions to software engineering education and diversity initiatives, such as the T-SITE program for transfer students, reflect her broader academic impact.

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Akramul Azimمشاهده پروفایل

دانشیار

Ontario Tech UniversityCanada

Dr. Akramul Azim is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, Faculty of Engineering and Applied Science. His research focuses on real-time systems, embedded software, safety-critical systems, IoT, software verification/validation, and applied machine learning. He leads the Real-Time Embedded Software Research (RTEMSOFT) Lab and has contributed to over 50 peer-reviewed publications. His work spans cybersecurity for automotive systems, edge computing optimization, and AI-driven software testing. Education: PhD (Electrical and Computer Engineering), University of Waterloo (2014) MSc (Computer Science and Engineering) (2009) BSc (Computer Science and Information Technology) (2006) Research Interests: Dr. Azim explores the intersection of real-time systems and machine learning, particularly in embedded environments. Key areas include: Autonomous vehicle safety Edge/IoT security Optimized task scheduling ML-enhanced software testing FPGA vulnerability mitigation Recent Research Trends: His publications (2023-2025) highlight advancements in LLM-based code optimization, lock contention mitigation in Java, energy-efficient vehicular networks, and safety-critical AI applications. Notable contributions include frameworks for situation-aware adaptation in ITS and reinforcement learning for edge task scheduling. Labs/Teams: Directs the RTEMSOFT Lab, collaborating on projects like AD-CGAN for anomaly detection and ESOps agile pipeline development.

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Andrea Capiluppiمشاهده پروفایل

دانشیار

University of GroningenNetherlands

Andrea Capiluppi is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, Department of Software Engineering — Bernoulli Institute. His research focuses on open source software, software development processes, natural language processing, and machine learning applications in software engineering. He holds roles including Member of the Board of Examiners and Member of the Governing Board of the Thematic Digital Competence Centre (TDCC) for NES. Research interests span open source technologies, software component analysis, software maintenance, and applying NLP to software traceability. His work bridges empirical studies with practical software engineering challenges, such as developer sentiment analysis and legacy code refactoring. He has published over 110 papers, contributing to areas like technical debt detection, code obfuscation impact, and academia-industry collaboration models. No awards are explicitly mentioned, but his leadership roles reflect academic and institutional contributions. He advises on software ecosystems and innovation dynamics, with ongoing projects involving automated software classification and traceability in DevOps environments.

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Caterina Urbanمشاهده پروفایل

پژوهشگر

InriaFrance

Caterina Urban is a Research Scientist at INRIA, affiliated with École Normale Supérieure in Paris, where she is a member of the ANTIQUE research team. She holds a PhD in Computer Science from École Normale Supérieure and completed her undergraduate and graduate studies at Università degli Studi di Udine with summa cum laude honors. She previously conducted postdoctoral research at ETH Zurich. PhD in Computer Science, École Normale Supérieure, Paris, France (2015) MSc in Computer Science, Università degli Studi di Udine, Italy (2011) BSc in Computer Science, Università degli Studi di Udine, Italy (2009) Her research spans formal methods, abstract interpretation, and static analysis, with a strong emphasis on ensuring reliability, fairness, and robustness in machine learning and data science software. She leads projects such as Lyra (on data science software), Libra (fairness certification), ForML, and SAIF. Her work bridges theoretical foundations with practical tool development, targeting real-world software challenges in AI safety. Her recent publications reveal a consistent focus on applying abstract interpretation to machine learning, data leakage, fairness, and program termination. Key themes include certified robustness, input usage analysis, and perfectly parallel verification, demonstrating both theoretical depth and practical impact. She has developed open-source tools like Libra and FuncTion. Best Poster Award at SOFSEM 2011 Artifact Evaluation Badges at OOPSLA 2020 Mentored award-winning undergraduate research (Global Undergraduate Awards 2022) She advises multiple PhD and postdoctoral researchers and has supervised numerous master’s and bachelor’s students. She is actively involved in the academic community, serving on program committees for PLDI, OOPSLA, and CAV, and is the general chair of iFM 2025. She teaches advanced courses at MPRI and international summer schools on abstract interpretation and formal methods. She leads research on data science software analysis, fairness in ML, and static verification of neural networks, often in collaboration with institutions across Europe. Her lab, part of the ANTIQUE team, fosters interdisciplinary work combining formal methods with AI and software engineering.

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Anıl Koyuncuمشاهده پروفایل

استادیار

Bilkent UniversityTurkey

Dr. Anıl Koyuncu is an Assistant Professor in the Department of Computer Engineering at Bilkent University. His research focuses on automated program repair, bug localization, and applying machine learning techniques to software engineering challenges. He actively contributes to academic service as a reviewer for journals like TOSEM and SoSyM, and serves on program committees for top conferences such as ICSE and ISSTA. He teaches courses including Automated Program Repair, Object-Oriented Software Engineering, and Software Design Patterns. His research group, the Automated Program Repair and Tools for Software Maintenance team, explores improving practitioner adoption of automated repair systems through data mining, program analysis, and fault localization. Key contributions include FixMiner for mining fix patterns and iFixR for bug-report-driven repair. Dr. Koyuncu has received the Best ERA Paper Award at APSEC 2018. His work emphasizes empirical studies on repair system evaluation, fault localization biases, and combining engineered features with learned embeddings for patch prediction.

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Jonathan D Victorمشاهده پروفایل

استاد

Cornell UniversityUnited States

Jonathan D Victor is Professor of Computational Neuroscience in Computational Biomedicine at the Institute for Computational Biomedicine, Professor of Neuroscience at the Brain and Mind Research Institute, and Fred Plum Professor of Neurology within the Department of Neurology at Weill Cornell Medical College (Cornell University). He serves as an Attending Neurologist at NewYork-Presbyterian Hospital and directs the Division of Systems Neurology and Neuroscience. His educational background includes: B.A. from Harvard University (1973) Ph.D. in Neurophysiology from The Rockefeller University (1979) M.D. from Cornell University Medical College (1980) Victor's research integrates computational neuroscience with clinical neurology, focusing on neural coding in sensory systems (vision, taste, smell), disorders of consciousness, and eye movement dynamics. He employs information theory, statistical modeling, and electrophysiology to decode how the brain processes natural stimuli and recovers from injury, with emphasis on perceptual spaces and neural network signatures. Analysis of his 2023-2025 publications reveals three dominant trends: computational modeling of sensory-motor transformations in natural behaviors (e.g., Drosophila locomotion), geometric frameworks for perceptual spaces (visual texture, depth processing), and EEG-based biomarkers for traumatic brain injury recovery (alpha coherence, thalamic stimulation). No scientific awards were explicitly documented in the source material. He secures major NIH/Foundation grants as Principal Investigator or Co-Investigator, including National Eye Institute projects on fixational eye movements (2022-2026), James S. McDonnell Foundation's Covid-19 consciousness recovery consortium (2020-2025), and National Cancer Institute's NeuroNex olfactory behavior study (2020-2026). These fund translational work bridging neural coding theory with clinical applications. Victor leads the Division of Systems Neurology and Neuroscience, leveraging collaborations with Weill Cornell's Brain and Mind Research Institute and Institute for Computational Biomedicine. His external relationships include consulting for Elsevier and Springer Science and Business Media on scientific publishing and data analysis.

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Giona Casiraghiمشاهده پروفایل

پژوهشگر

ETH ZurichSwitzerland

Giona Casiraghi is a Senior Researcher at ETH Zurich specializing in network science and complex systems, with a primary focus on resilience modeling in social organizations and data-driven network analysis. His work bridges theoretical advances in statistical network models with practical applications in supply chain management, open-source software ecosystems, and online social dynamics. His research interests center on developing quantitative methods for analyzing complex systems, particularly through the generalized hypergeometric ensemble of random graphs (gHypEG). Casiraghi's work spans multiple disciplines including network science, statistical physics, data science, and resilience theory, with particular expertise in temporal network analysis, multi-edge networks, and zero-inflation models for sparse networks. His research group develops the ghypernet R package , providing open-source tools for network regression and inference. Analysis of his recent publications reveals a strong trend toward applying network science to real-world resilience problems, particularly in pharmaceutical supply chains and social organizations. His 2025 Science paper on US tariffs threatening medicine supply chains exemplifies this practical turn, while his methodological work on zero-inflated network models (PNAS Nexus 2025) demonstrates continued theoretical innovation. Casiraghi frequently collaborates with Frank Schweitzer and others in the Systems Group at ETH Zurich, producing interdisciplinary work that bridges computer science, economics, and social science. Casiraghi's research has significant implications for understanding how social organizations withstand shocks and how supply chains can be made more resilient to disruptions. His work on the gHypEG framework provides foundational tools for network scientists across multiple disciplines, while his applied research offers concrete insights for policymakers and industry practitioners dealing with complex system failures. He has contributed to numerous projects examining online migration after community bans, developer productivity in open-source projects, and reconstruction of social relations from interaction data. His research methodology typically combines large-scale data analysis with advanced statistical modeling, often developing new network analysis techniques to address specific research questions.

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Antonio Martiniمشاهده پروفایل

استاد

University of OsloNorway

Professor Antonio Martini is a faculty member at the Department of Informatics, University of Oslo , specializing in Software Engineering and Technical Debt management. His research spans microservices , machine learning , visual analytics , and agile methodologies . His research interests focus on: Technical Debt classification and prioritization Machine Learning applications in software development Visual Analytics for Decision Support Scalability in open and serverless systems Notable trends in his publications (2025–2018) include leveraging transformer-based NLP for debt analysis, security debt frameworks, and visual analytics in supply chain environments. He has contributed to tools like AnaConDebt and Skuld for debt tracking and estimation.

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Zhongyan Chenمشاهده پروفایل

پژوهشگر

The University of ManchesterUnited Kingdom

Zhongyan Chen is a Researcher in the Department of Computer Science at the University of Manchester. Their work focuses on software testing, open-source software, and technical debt analysis. Chen holds a Bachelor of Science in Software Engineering from Wuhan University (2015-2019) and a Master of Science in Advanced Computer Science from the University of Manchester (2019-2020). Research interests include evaluating test smells in open-source projects, assessing technical debt through developer actions, and improving software testing education for students and professionals. Their work has been published in peer-reviewed conferences like IFIP-ICTSS and is supported by datasets publicly available on Figshare. Key datasets include mappings of test code changes across commits, smell detection results, and analysis of commits containing test code modifications. These datasets are openly accessible via DOIs like 10.48420/22583665 and 10.48420/22583245.

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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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Nan Niuمشاهده پروفایل

دانشیار

University of CincinnatiUnited States

Dr. Nan Niu is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Cincinnati. He specializes in requirements engineering, software traceability, and model-driven approaches. His work focuses on machine learning applications, continuous deployment, and testing scientific software. Dr. Niu holds a Ph.D. in Computer Science from the University of Toronto (2010, supervised by Steve Easterbrook), an M.Sc. from University of Alberta (2006), and a B.Eng. from Beijing Institute of Technology (2004). Research interests include software engineering challenges in AI/ML systems, automated refactoring, and environment-driven requirements engineering. His research is funded by agencies like NSF, NSA, and industry partners like P&G. Recent publications address topics like POS tagging in code, GUI reuse via vision-language models, and privacy concerns in mobile apps. Teaching: Software Engineering (EECE3093C), Requirements Engineering (CS5127/6027), and Large-Scale Software Engineering (CS5128/6028) Labs/Teams: Active in requirements traceability and scientific software testing Awarded over 15 honors including NSF CAREER Award (2014-2019), Best Paper Awards at RE (2016,2018,2021), and teaching excellence recognition.

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Mika Mäntyläمشاهده پروفایل

استاد

University of HelsinkiFinland

Mika Mäntylä is a Professor at the Department of Computer Science, University of Helsinki, affiliated with the Faculty of Science. His research focuses on software engineering, testing, log analysis, and systematic literature reviews. He leads projects such as CRITICAL (cultivating critical reading in the internet era) and a microservice anomaly detection initiative funded by the Academy of Finland. His work spans technical domains like software log anomaly detection, meta-learning applications, and social media credibility evaluation. Notable tools include Credtwi (a browser plugin for credibility assessment) and LogLead (a log analysis system). Key grants include SRC Strategic Research Council funding (2023–2026) and Academy Project funding (2023–2026). Mika’s research bridges technical and societal challenges, addressing software quality, automated anomaly detection, and user-centric credibility evaluation. His publications often emphasize empirical methods, tool development, and interdisciplinary approaches.

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