جست‌وجوی استاد و پژوهشگر

Jie Wuمشاهده پروفایل

استادیار

• AI for Software Engineering
• Software Engineering for AI
• Large Language Models
• +۵ مورد دیگر

Jie Wu is an Assistant Professor in the Department of Computer Science at Michigan Technological University, a Carnegie R1 (Very High Research Activity) institution. Previously, he was a postdoctoral researcher at the University of British Columbia working with Dr. Fatemeh Fard at the intersection of Software Engineering and AI. Dr. Wu received his PhD in Systems Engineering from George Washington University. His undergraduate and master's studies were both in Computer Science at Shanghai Jiao Tong University's elite ACM Class program. Before academia, he worked for nearly a decade as a software engineer in the industry at Snap Inc., Microsoft, and ArcSite (a startup). Dr. Wu's research focuses on Trustworthy AIware, with particular interest in transforming "AI for Software Engineering" and "AI system development" from art into rigorous science and engineering disciplines. His work emphasizes human-centered AI, AI alignment, and practical software engineering, grounded in a systems-thinking mindset. His primary research areas include AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models (LLMs), Data Science, and Systems Science and Engineering. His recent publications demonstrate a strong focus on evaluating and improving communication capabilities of code-generating LLMs, automated program repair using LLMs, and applying AI to software engineering challenges. His work often bridges theoretical foundations with practical applications in industry settings, with notable contributions including the HumanEvalComm benchmark for evaluating communication skills in code generation. Distinguished Paper Award Candidate at CAIN 2024 for V-Model research Reviewer for top-tier journals including IEEE TSE and ACM TOSEM Program Committee Member for RAIE 2025, CAIN 2025, and SANER 2025 Dr. Wu is actively recruiting PhD students to join his research group at Michigan Tech to work at the intersection of Software Engineering and AI. He is passionate about bridging academic research and industry practice to accelerate innovation and create meaningful societal impact, welcoming collaborations with industry partners interested in applying cutting-edge AI research to real-world challenges.

• Mobile Software Engineering
• Intelligent Software Engineering
• Software Security
• +۶ مورد دیگر

Xiaoyu Sun is a Lecturer in the School of Computing at Australian National University (ANU), specializing in Software Engineering with a focus on Mobile Software Engineering and Intelligent Software Engineering. She holds a PhD from Monash University (2023) and a Bachelor's degree in Computer Science from Beijing Normal University (2016). Her research emphasizes applying static code analysis, dynamic testing, and NLP techniques to enhance software security and reliability, particularly in Android systems. Current projects include tools for detecting compatibility issues and privacy leaks in mobile apps. Her research interests span code generation frameworks (e.g., A^3-CodGen), security management in open-source projects, and AI-enhanced software development practices. Collaborations with tech giants like Bytedance and Alibaba highlight her industry engagement. Xiaoyu is Co-Investigator in the Tech4HSE project (2025-2027), developing AI-based monitoring systems for workspace safety. She has published in top venues including ICSE, ASE, and IEEE Transactions on Software Engineering. Her work bridges academia and industry, addressing challenges in mobile app security, code reuse efficiency, and developer toolchain innovation. Ongoing efforts focus on AI-driven solutions for software engineering tasks and fostering transparent privacy practices in open-source AI applications.

Alireza Mohammadinodooshanمشاهده پروفایل

پژوهشگر ارشد

• Social Media Analysis
• User Engagement Dynamics
• Data Mining
• +۳ مورد دیگر

Alireza Mohammadinodooshan is a Postdoctoral Fellow at Linköping University's Department of Computer Science (IDA), Sweden, working within the Database and Information Technology (ADIT) research group. He contributes to the Wallenberg AI, Autonomous Systems and Software Program (WASP) – Sweden's largest individual research initiative – focusing on data-driven analysis of social media engagement dynamics across Twitter, Facebook, and Instagram platforms. His research centers on quantifying how political bias, news reliability, and content-agnostic factors shape temporal user engagement patterns. Key interests include social media analysis, user engagement dynamics, data mining, information systems, network science, and artificial intelligence, with emphasis on cross-platform comparative studies and algorithmic amplification effects in news consumption. Analysis of his 15 most recent publications reveals consistent focus on temporal modeling of engagement decay, multi-format content interaction (photos/videos/albums), and the interplay between news source characteristics and user behavior. His methodological approach combines large-scale dataset analysis with network theory to identify virality predictors and platform-specific engagement mechanics. No scientific awards were documented in available sources. No advising roles or research grants were referenced in the provided materials. He operates within the ADIT research group at Linköping University, which specializes in advanced database and information systems for the digital society. This group forms part of IDA's broader WASP-affiliated ecosystem focused on AI-driven autonomous systems, enabling interdisciplinary collaboration on large-scale data challenges in social computing.

Jia Liمشاهده پروفایل

استادیار

• Programming Language Processing
• Code Understanding
• Code Generation
• +۷ مورد دیگر

Jia Li is an Assistant Professor at the College of AI, Tsinghua University, where they lead the Tsinghua University Programming Language Processing Group (THU-PLP). They completed their PhD at Peking University in 2025 under the supervision of Prof. Zhi Jin and Prof. Ge Li. Dr. Li's research focuses on Programming Language Processing (PLP), which aims to develop artificial intelligence techniques for understanding and generating source code. Their work spans two main areas: foundation models for PLP and applications of PLP in software development and beyond. They develop new model architectures, training strategies, inference approaches, and evaluation metrics to improve code understanding and generation capabilities. Their application research explores how PLP can enhance software development efficiency through code generation, test generation, and code optimization, as well as its applications in embodied AI and neuroscience. Dr. Li's recent publications demonstrate a strong focus on advancing code generation and understanding through large language models. Their work addresses key challenges in repository-level code completion, class-level code translation, vulnerability detection, and benchmarking evolving code generation capabilities. They've made significant contributions to developing efficient models like aiXcoder-7B and creating comprehensive benchmarks like EvoCodeBench and ClassEval-T. NeurIPS 2025 Spotlight Paper (3.2% acceptance rate) for "SATURN: SAT-based Reinforcement Learning to Unleash Language Model Reasoning" Dr. Li actively mentors students and researchers, seeking highly-motivated interns to join the THU-PLP research group. They have established collaborations with researchers at Peking University, as evidenced by their joint publications with supervisors Prof. Zhi Jin and Prof. Ge Li. Dr. Li leads the Tsinghua University Programming Language Processing Group (THU-PLP), which focuses on cutting-edge research at the intersection of programming languages and artificial intelligence. The group maintains active GitHub repositories for their research projects, including EvoCodeBench, SkCoder, and CodeEditor, demonstrating their commitment to open science and reproducible research.

Michael W. Godfreyمشاهده پروفایل

استاد

• Software Engineering
• Program Comprehension
• Mining Software Repositories
• +۵ مورد دیگر

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

James Pattenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Software Engineering
• Source Code Analysis
• +۳ مورد دیگر

James Patten is a Research Fellow at the University of Limerick , affiliated with the Department of Computer Science & Information Systems and the research center Lero – the Irish Software Research Centre . His work focuses on applying machine learning and evolutionary computation to enhance software quality, with a specific emphasis on code duplication detection and refactoring. Primary Affiliation: Department of Computer Science & Information Systems, University of Limerick Research Center: Lero – the Irish Software Research Centre Patten’s research spans two major domains: Software Engineering: Code duplication elimination, clone detection using BERT-based models, evolutionary algorithms for codebase analysis Gender Equity: Active participant in the WiSTEM2D initiative, exploring systemic interventions to improve female representation in STEM fields His publications reflect a dual focus on scalable software analysis techniques (e.g., ensemble inference for clone detection) and social science studies on gender dynamics in technology education. Notably, Patten combines technical rigor with societal impact by addressing both software reliability and diversity challenges.

Zhen Ming Jiangمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at the Department of Electrical Engineering & Computer Science, York University, Canada. He earned his Ph.D. (2013) from Queen's University and MMath/BMath degrees from the University of Waterloo. Research Focus: Software Engineering for AI, Performance Engineering, Logging Practices, and Software Visualizations. Education: Ph.D. in Computer Science, Queen's University MMath in Computer Science, University of Waterloo BMath in Computer Science, University of Waterloo His research explores engineering rigor in AI-powered applications, performance optimization in foundation model-driven systems, and efficiency improvements in large-scale software. Recent work analyzes logging practices, code cloning in blockchain, and AIOps models. He has received prestigious awards including the NSERC Discovery Accelerator Supplements (2020) and multiple Best Paper Awards at ICST, ICSE, and MSR. He served on program committees for ICSE, ICSME, and ICPE, and reviewed for top journals like IEEE Transactions on Software Engineering.

David W. Binkleyمشاهده پروفایل

استاد

• Software Engineering and Testing
• Program Slicing and Clustering
• Information Retrieval in Software Engineering
• +۶ مورد دیگر

David W. Binkley is a Professor in the Department of Computer Science at Loyola University Maryland. His research focuses on Software Engineering and Testing Program Slicing and Clustering Information Retrieval Techniques in Software Engineering Safety-Critical Systems Code Clone Detection Recent work includes dynamic slicing of WebAssembly binaries and adaptive change recommendation systems using association rules. He has contributed extensively to empirical studies on dependence clusters, testability transformations, and observational slicing techniques. Key collaborations include institutions like Simula Research Laboratory (Norway) and NIST (National Institute of Standards and Technology). His publications span top-tier venues such as IEEE Transactions on Software Engineering, ACM TOPLAS, and ICSE.

Alessandra Gorlaمشاهده پروفایل

دانشیار

• Malware detection for mobile applications
• Automatic software repair
• Software testing
• +۴ مورد دیگر

Alessandra Gorla is an associate researcher professor at IMDEA Software Institute in Madrid, Spain, with a strong background in software engineering research. She previously worked as a postdoctoral researcher with Andreas Zeller at Saarland University in Germany and completed her PhD under Mauro Pezzè at the University of Lugano in Switzerland. Her research bridges theoretical foundations with practical applications in mobile software systems. Her research focuses on malware detection for mobile applications, automatic software repair, software testing and analysis. She has developed techniques for detecting behavior anomalies in graphical user interfaces, identifying third-party libraries in mobile apps, and leveraging intrinsic software redundancy for reliability. Her work spans both Android and iOS ecosystems, with particular attention to permission systems, release practices, and security implications. Analysis of her recent publications reveals a strong trend toward mobile application security and analysis, with increasing focus on iOS systems alongside traditional Android research. Her work combines static and dynamic analysis techniques, often incorporating natural language processing for comment analysis and test generation. There's a clear progression from foundational work on intrinsic software redundancy to more applied research on mobile security and testing. FRITZ-KUTTER AWARD! for PhD thesis on Automatic Workarounds BEST PAPER AWARD! for Search-based Security Testing of Web Applications BEST STUDENT POSTER AWARD! for Automatic Workarounds as Failure Recoveries Dr. Gorla actively mentors students and seeks motivated individuals for internship and PhD opportunities in software engineering. She has served in various organizational roles including Tool Demonstrations co-chair for FSE 2016, Artifact Evaluation co-chair for ESSoS 2016 and ISSTA 2016, and multiple program committee positions at top software engineering conferences. Her work has been supported through collaborations with major research institutions and industry partners. At IMDEA Software Institute, Dr. Gorla leads research on mobile application analysis, particularly focusing on behavioral analysis of Android and iOS applications. Her CHABADA prototype for clustering Android apps by description topics and identifying API usage outliers demonstrates her practical approach to malware detection. She also investigates intrinsic software redundancy for building more resilient systems.

Ying Zouمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence for Software Engineering
• Software Evolution
• +۲ مورد دیگر

Dr. Ying Zou is a Professor in the Department of Electrical and Computer Engineering at Queen's University's Smith Engineering faculty in Kingston, Ontario, Canada. With an extensive publication record spanning from 2018 through 2025, Dr. Zou has established herself as a leading researcher in empirical software engineering with a growing focus on AI integration. Dr. Zou's research focuses on Software Engineering , Artificial Intelligence for Software Engineering (AI4SE) , Software Evolution , Software Analytics , and Empirical Software Engineering . Her work bridges theoretical approaches with practical applications, examining developer behavior, code quality improvement, and AI techniques for software engineering tasks. Recent publications demonstrate a clear progression from traditional empirical studies toward more AI-centric approaches, particularly in code refactoring, type inference, and performance analysis. Analysis of Dr. Zou's publication trends reveals a strategic evolution in her research focus. Early work centered on empirical studies of Stack Overflow and GitHub, while recent publications increasingly integrate large language models and AI techniques for software engineering tasks. Her research spans multiple dimensions including code quality, developer productivity, open source community dynamics, and performance optimization, with consistent methodological rigor in empirical validation. Dr. Zou has served in numerous leadership roles across major software engineering conferences including ASE, ICSE, and ESEC/FSE. She has been a Program Committee member for multiple tracks and conferences, and notably served as New Faculty Mentoring Co-Chair for ESEC/FSE 2026. Her service to the community extends to organizing conference tracks, chairing sessions, and mentoring new researchers in the field.

• Software Engineering
• Source Code Analysis
• Software Clone Detection
• +۱۲ مورد دیگر

James R. Cordy is a Professor in the School of Computing at Queen's University, Faculty of Engineering and Applied Science, Kingston, Canada. He is a leading researcher in software engineering, with a focus on source code analysis, software clone detection, model-driven engineering, and program transformation. He has been actively publishing since 1977, with a sustained record of contributions in top-tier venues such as ICSE, MoDELS, and WCRE. His research interests include software clone detection, model transformation, Simulink models, source transformation, software maintenance, and grammatical inference. He has developed and contributed to influential tools such as TXL and NiCad, and his work often involves empirical studies and tool evaluation in real-world software systems. The most recent articles highlight trends in model transformation, clone detection, verification of state machines, and migration of legacy systems. His work increasingly integrates formal methods and empirical validation, particularly in automotive and safety-critical domains. He has also explored applications in healthcare software, such as artificial pancreas systems. Most Influential Paper Award, SCAM 2001 (awarded in 2019) He has advised numerous students, including Manar H. Alalfi, Matthew Stephan, and Chanchal K. Roy, who have co-authored multiple publications with him. His research is often collaborative, involving teams from Queen's University and other institutions. He has also contributed to workshops and special issues, demonstrating leadership in the software engineering community.

• Software Engineering
• Cybersecurity
• Behavioral Biometrics
• +۳ مورد دیگر

Dr. Daqing Hou is a Professor at Clarkson University's Coulter School of Engineering & Applied Sciences, affiliated with the Department of Electrical & Computer Engineering and Computer Science. His research bridges software engineering, cybersecurity, behavioral biometrics, and education research. He earned his Ph.D. in Computing Science from the University of Alberta, and M.S./B.S. in Computer Science from Peking University. Research focuses on improving software development efficiency through project-based learning (PjBL), empirical studies, and human factors. Key areas include behavioral biometrics (keystroke/mouse dynamics), smart housing/energy systems, and cybersecurity solutions. He has pioneered tools like CReN for managing code clones and has contributed to Eclipse/UIMA frameworks. Recent publications emphasize behavioral authentication methods, mobile biometrics evaluation, and framework design recommendations. His work has been recognized with awards including IEEE ICSME 2014 Best Paper Nomination and IBM Innovation Awards (2005/2007). Teaching spans software engineering, GUI design, databases, machine learning, and compilers. He advises students on topics like API usability, security systems, and programming language tools.

Thomas Leichمشاهده پروفایل

استاد

• Software Product Line Engineering
• Requirements Engineering
• IT Security
• +۱۱ مورد دیگر

Prof. Dr.-Ing. Thomas Leich holds the Volkswagen Financial Services Endowed Professorship for Business Informatics, specifically Requirements Engineering, at Harz University of Applied Sciences. He is affiliated with the Faculty of Automation and Information, focusing on software variability and configurable systems. University: Harz University of Applied Sciences School: Faculty of Automation and Information Academic Rank: Professor Research Interests span software product line engineering, IT security, Industry 4.0, and feature-oriented programming. His work bridges academic innovation with industrial applications, particularly in secure and scalable systems for automotive and enterprise contexts. Recent Publications emphasize security in configurable systems, empirical software engineering studies, and automotive platform management. Articles often involve collaborations with teams at Otto von Guericke University and international institutions. Scientific Awards include the Hugo Junkers Research Award (2013), the Wissenschaftspreis der Wernigeröder Stadtwerkestiftung (2017), and the 2012 Faculty Research Award from the University of Magdeburg. Supervision of over 20 theses since 2008, spanning topics in ERP security, program comprehension, and embedded systems. He leads the EXPLANT project, funded by DFG, and contributes to FeatureIDE , an extensible framework for feature-oriented development.

Cristina Lopesمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Distributed Virtual Environments
• +۵ مورد دیگر

Cristina Lopes is a Professor of Informatics at the Donald Bren School of Information and Computer Sciences , University of California, Irvine. She holds administrative roles including Director of the Master of Software Engineering Program and former Director of the Institute for Software Research . Her research focuses on software engineering, programming languages, and distributed systems, with contributions to open-source projects like OpenSimulator and AspectJ. She has authored the influential book Exercises in Programming Style . Education: PhD from Northeastern University, MS/BS from Instituto Superior Técnico (Portugal). Awards include IEEE Fellow (2019), ECOOP Test of Time Award (2017), and ACM Distinguished Scientist (2011). She co-founded a virtual reality company for urban redevelopment and leads NSF-funded projects, including the prestigious CAREER Award. Research Interests: Large-scale software systems, aspect-oriented programming, code clone detection, and applying AI to programming challenges. Her work bridges theory and practice, with applications in urban simulation and healthcare systems. Awards and Grants: Over 15 awards including national and international distinctions. Grants focus on scalable software tools and environmental sustainability in computing. Labs/Teams: Leads the Software Engineering Research Group and collaborates with industry partners on open-source infrastructure and AI-driven development tools.

• Software Engineering
• Programming Languages
• Distributed Virtual Environments
• +۳ مورد دیگر

Crista Lopes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She serves as Director of the Master of Software Engineering Program and previously led the Institute for Software Research. Her research focuses on software engineering, programming languages, and distributed virtual environments. Dr. Lopes holds editorial roles as Editor-in-Chief of The Art, Science, and Engineering of Programming and on the Advisory Board of PACMPL . She has received notable awards including IEEE Fellow (2019), ECOOP Test of Time Award (2017), and ACM Distinguished Scientist (2011). Her teaching spans courses like INF 212 Programming Languages II and INF 225 Information Retrieval . Recent research includes AI-assisted programming tools, code analysis frameworks like Sourcerer , and studies on climate-conscious conference practices. She actively mentors students in UCI’s Software Engineering doctoral program.