جست‌وجوی استاد و پژوهشگر

Ying Wangمشاهده پروفایل

دانشیار

• Dependency management
• Software ecosystem
• Software refactoring
• +۷ مورد دیگر

Dr. Ying Wang is an Associate Professor and doctoral supervisor at the Software College of Northeastern University (China), where she has been working since February 2019. She serves as Assistant Dean at the School of Software and is an active member of several CCF committees including the System Software Committee, Software Engineering Committee, Open Source Development Committee, and Women's Committee. Dr. Wang received her Ph.D. in Software Engineering from Northeastern University in January 2019 under the supervision of Professor Zhiliang Zhu. She completed postdoctoral research at the Hong Kong University of Science and Technology (HKUST) from 2022 to 2023 under Professor Shing-Chi Cheung and was a visiting scholar at Microsoft Research Asia through the StarTrack Program in 2021. Her research focuses on intelligent software development technologies, large AI models, open source software big data analysis, and software supply chain security. She has made significant contributions to the governance of open source software ecosystems across multiple programming languages including Java, C#, Python, Go, JavaScript, Android, and Rust. Her work has led to the development of practical tools like 'League of Legends' for monitoring dependency defects in open source ecosystems, with several technologies commercialized by Huawei and Microsoft. Dr. Wang's recent publications demonstrate her expertise in cross-language dependencies, software component analysis, software refactoring, and the application of large language models in software engineering. Her work spans both theoretical foundations and practical applications, with a strong emphasis on real-world impact through industry collaboration. Among her notable achievements are the ACM SIGSOFT Distinguished Paper Awards at ICSE 2021 and ESEC/FSE 2023, making her the first researcher from Northeastern University to receive this honor. She has also received multiple awards for her doctoral dissertation and prototype implementations. Dr. Wang actively contributes to the academic community as an Associate Editor for IEEE Transactions on Software Engineering and serves on program committees for top conferences including ASE, ICSE, and ESEC/FSE. She mentors a large group of doctoral and master's students, with many alumni securing positions at major technology companies including Huawei, Microsoft, Alibaba, and Tencent.

Rui Abreuمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Debugging
• +۶ مورد دیگر

Rui Abreu is a Professor at the Faculty of Engineering of the University of Porto (FEUP), Portugal, with extensive expertise in software quality, testing, and debugging. Previously, he served as Associate Professor at IST-ULisbon and Assistant Professor at the University of Porto. His research bridges academia and industry through roles including Visiting Researcher at Google NYC (2019-2020) and co-founding DashDash, a $9M Series A-funded startup for spreadsheet-based web app development. His educational background includes a Ph.D. in Computer Science - Software Engineering from Delft University of Technology and an M.Sc. in Computer and Systems Engineering from the University of Minho. His research focuses on automating software testing and debugging , with growing emphasis on quantum software testing, vulnerability detection, and AI-assisted development tools. Recent work explores large language models for loop invariant generation, interpretable vulnerability reports, and quantum mutation testing. His publication trends reveal a strong shift toward security-critical systems and emerging computing paradigms , with 30% of recent papers addressing quantum software challenges and 45% focusing on vulnerability detection/repair. The work consistently combines static/dynamic analysis with machine learning, targeting practical tool development for real-world engineering problems. 6 Best Paper Awards Distinguished Paper Award at ESEC/FSE 2019 Abreu actively mentors through conference committees (serving on 12+ program committees in 2024-2025) and industry engagement. His DashDash venture demonstrates successful technology transfer, while Google collaboration advanced C/C++ security tooling. Current work includes quantum software metrics and security commit standardization. He leads research teams focused on software quality automation, with recent projects including GZoltarAction (GitHub fault localization bot) and Maestro (vulnerability repair benchmarking platform). Future directions emphasize scalable security analysis for quantum systems and human-AI collaboration in debugging workflows.

Xiaoyuan Xieمشاهده پروفایل

استاد

• software testing
• fault localization
• AI for software engineering
• +۶ مورد دیگر

Xiaoyuan Xie is a Professor at the School of Computer Science, Wuhan University, where he leads a research group focused on software engineering, particularly in software testing, fault localization, and the intersection of AI and software engineering. His work bridges theoretical foundations with practical applications in software quality assurance and development processes. His research interests span software testing, fault localization, program slicing and analysis, debugging techniques, search-based software engineering, evolutionary computing, and machine learning applications in software engineering. He has pioneered work in metamorphic testing, particularly for AI systems, and developed innovative approaches for fault localization that consider exception trigger streams and parallel debugging contexts. Xie's publication trend shows a strong focus on applying AI techniques to software engineering problems, with recent work emphasizing metamorphic testing for AI systems, code search, debugging assistance, and fault localization enhanced by deep learning. His research bridges traditional software engineering with modern AI techniques, creating novel solutions for testing and debugging complex software systems. ICST 2025 Most Influential 10-years Journal First Paper Award Multiple ACM SIGSOFT Distinguished Paper Awards (2024, 2022, 2021) CCF Open Source Innovation Competition First Prize 2024 CCF NASAC Software Innovation Award for Young Researchers 2021 ACM SIGEVO HUMIES 2017 Silver Medal Luojia Young Scholar, Wuhan University 2016 Professor Xie has supervised numerous PhD and Master's students who have gone on to prestigious positions at companies like Alibaba, Tencent, and ByteDance, as well as academic institutions including Yale University and the Hong Kong University of Science and Technology. His research is supported by multiple national grants, including NSFC重点项目 (key projects), 国家重点研发计划 (key R&D programs), and the Research Fund for International Excellent Young Scientists from NSFC.

Xiaofei Xieمشاهده پروفایل

استادیار

• Program Analysis
• Software Testing
• Vulnerability Detection
• +۴ مورد دیگر

Xiaofei Xie is an Assistant Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he has been employed since 2022. Prior to this position, he was a postdoctoral researcher at Nanyang Technological University in Singapore from 2018 to 2021. His research primarily focuses on program analysis, software testing, vulnerability detection, and quality assurance of AI systems. SMU is ranked No. 9 (No. 5 in Asia) in the Software Engineering category according to CSRankings. Dr. Xie's research interests span multiple critical areas in software engineering and AI systems. His work on program analysis includes detecting non-termination bugs and developing practical methods like EndWatch for real-world software. In software testing, he has made significant contributions to deep learning systems testing, autonomous driving systems testing, and smart contract security. His research on vulnerability detection encompasses various aspects of AI security, including backdoor attacks, adversarial examples, and security testing for web-based deep learning frameworks. His quality assurance work for AI systems includes developing metrics for robustness evaluation and creating testing methodologies for diverse AI applications. Dr. Xie's publication record shows a strong trend toward integrating large language models with traditional software engineering techniques. His recent work demonstrates increasing focus on testing autonomous systems, securing AI models, and applying advanced machine learning techniques to traditional software engineering problems. The research spans multiple domains including deep learning frameworks, smart contracts, autonomous driving systems, and federated learning environments. Among his notable achievements are multiple ACM SIGSOFT Distinguished Paper Awards (ASE 2019, ASE 2023, ISSTA 2022), the ACM Tianjin Doctoral Dissertation Award 2019, and the Best Paper Award at APSEC 2020. His work has been accepted to top-tier conferences including ICSE, FSE, ASE, ISSTA, and security venues like USENIX Security. Dr. Xie actively serves the academic community as a PC co-chair for ICECCS 2025 and as a program committee member for numerous prestigious conferences including ICSE, FSE, ASE, ISSTA, and AAAI. He has also organized workshops such as the Workshop on AI and Software Testing/Analysis (AISTA) and served as Guest Editor for special issues on AI security. His service demonstrates leadership in bridging software engineering with AI and security research communities.

Yi Liمشاهده پروفایل

دانشیار

• Software Engineering
• Program Analysis
• Automated Reasoning
• +۲ مورد دیگر

Yi Li is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He leads the Software Reliability and Security Lab (SRSLab@NTU) which focuses on program analysis and automated reasoning techniques for software engineering and security applications. Dr. Li received his BComp degree in Computer Science from the National University of Singapore in 2011, followed by MSc (2013) and PhD (2018) degrees in Computer Science from the University of Toronto. His educational background has positioned him at the forefront of software reliability research. His research interests span software engineering, program analysis, automated reasoning, and formal methods, with particular focus on software reliability, security, and smart contract analysis. Dr. Li's work develops practical solutions for constructing high-quality software systems that are both reliable and sustainable. His research has identified critical issues in API documentation, smart contract security, program termination, and library compatibility across multiple programming ecosystems. Dr. Li's publications demonstrate consistent contributions to software engineering research over the past five years, with recent work focusing on smart contract analysis, API documentation verification, and program termination detection. His research methodology often combines static and dynamic analysis techniques with machine learning approaches to address challenging problems in software reliability and security. Five ACM Distinguished Paper Awards at top conferences Two Best Artifact Awards recognizing research reproducibility Publications at premier venues including ASE, ISSTA, FSE, and ICSE Dr. Li actively contributes to the software engineering community through service on program committees of flagship conferences (ICSE, FSE, ASE, ISSTA) and co-chairing program committees for ICFEM'23, ICECCS'20, SEAIS'22, and ICFEM'19 Doctoral Symposium. His lab, SRSLab@NTU, has developed multiple influential tools including InvCon, DocCon, and EndWatch, while fostering research in software reliability and security for emerging technologies.

Darko Marinovمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Regression Test Prioritization
• +۴ مورد دیگر

Darko Marinov is a Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign, where he conducts research focused on improving software quality through advanced testing techniques. His work addresses fundamental challenges in software testing including flaky tests, regression test prioritization, and configuration testing. Dr. Marinov's research interests span multiple areas of Software Engineering , with particular emphasis on: Software Testing and Quality Assurance Regression Test Prioritization and Selection Flaky and Non-Deterministic Tests Configuration and Variability Testing Test Suite Optimization Software Reliability and Defect Detection His extensive publication record demonstrates consistent contributions to the field, with research trends showing evolution from foundational testing techniques to addressing modern challenges in software testing, particularly focusing on reliability issues in continuous integration environments and the growing complexity of test suites in large software systems. Dr. Marinov has received numerous prestigious awards recognizing the impact of his work: Two ACM SIGSOFT Impact Paper awards (2012, 2019) ASE Most Influential Paper Award (2015) FSE Test-of-Time Honorable Mention (2024) Seven ACM SIGSOFT Distinguished Paper awards CHI Best Paper Award (2017) Elected Fellow of Automated Software Engineering (2023) His research has been generously supported by major technology companies and government agencies including NSF, Boeing, Facebook, Google, Huawei, IBM, Intel, Microsoft, Qualcomm, Samsung, and SRC. Dr. Marinov actively contributes to the academic community through conference organization, serving as Area Chair for ASE 2025 and Program Committee member for multiple conferences including FSE 2026. His mentoring excellence has been recognized through various awards, highlighting his commitment to guiding the next generation of software engineering researchers.

Alessio Gambiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Software Testing
• Self-Driving Cars
• +۴ مورد دیگر

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Zhi Jinمشاهده پروفایل

استاد

• Requirements Engineering
• Self-Adaptive Systems
• Learning from Source Code
• +۵ مورد دیگر

Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.

Reid Holmesمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Code Search
• +۷ مورد دیگر

Reid Holmes is a Professor in the Software Practices Lab at the University of British Columbia's Department of Computer Science, Faculty of Science. With over 15 years of academic experience, he has progressed from Assistant Professor at the University of Waterloo (2010-2015) to Associate Professor (2015-2022) and now full Professor (2022-present) at UBC. His research spans multiple dimensions of software engineering with a strong emphasis on the human aspects of development. Dr. Holmes' research interests focus on understanding the problems software engineers encounter when creating and evolving software systems. His work examines software testing and validation, source code reuse, code search, context-sensitive example location, API understanding, speculative analysis, code review, and team awareness. He believes that by better understanding how people create, explore, evolve, and reason about software systems, we can enhance developers' effectiveness and improve software quality. His research is characterized by its practical application to real-world software development challenges. His recent publications demonstrate a consistent focus on improving developer productivity through better tools and understanding of developer behavior. The publications reveal a trajectory from traditional software engineering topics toward newer areas involving AI-assisted development, with particular attention to how generative AI affects workflow and collaboration. His work consistently bridges theoretical software engineering concepts with practical developer experience considerations. ACM SIGSOFT Distinguished Paper Award (multiple times) FSE 2024 Most Impactful Paper Award 2024 ICSE Most Influential Paper Award Winner of the 2008 MSR Mining Challenge As an educator and mentor, Dr. Holmes has supervised numerous graduate students who have gone on to successful careers in academia and industry. He has served on program committees for major software engineering conferences including ICSE, FSE, and MSR, and has been an Associate Editor for TSE since 2016. His work on developer tools like CodeShovel, Devy, and Baker demonstrates his commitment to creating practical solutions that address real developer pain points.

Reyhaneh Jabbarvandمشاهده پروفایل

استادیار

• Neural Program Analysis
• Software Testing
• Bug Localization
• +۴ مورد دیگر

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Intelligent CAT Lab. Her research focuses on improving software quality, reliability, and maintenance through neuro-symbolic approaches that combine AI techniques with formal methods. Her research interests span Neural Program Analysis, Software Testing (with emphasis on mobile apps and autonomous software), Bug Localization, and Applied Optimization for Software Analysis. She has made significant contributions to the fields of energy testing for Android applications, neuro-symbolic approaches for code analysis, and large language models for software engineering tasks. Dr. Jabbarvand's recent publications reveal strong trends in applying machine learning to software engineering problems, particularly using neuro-symbolic methods to bridge the gap between deep learning and formal program analysis. Her work on code translation, test flakiness, and test oracle generation demonstrates her focus on practical applications of AI in software development workflows. Google PhD Fellowship in Programming Technology and Software Engineering Rising Star in EECS NSF CAREER Award Dr. Jabbarvand has received research funding from multiple sources including NSF, IBM Research, and C3.ai. She actively mentors students through her Intelligent CAT Lab and has served on numerous program committees for major software engineering conferences including ICSE, FSE, and ISSTA. She teaches courses on Advanced Topics in Software Engineering, ML for Code, and Software Engineering I. Her lab focuses on neuro-symbolic approaches to software engineering problems, bringing together PhD, undergraduate, and high school students to tackle challenges in AI-assisted software development and testing.

• Information Retrieval
• Data Engineering
• Peer-to-Peer Systems
• +۳ مورد دیگر

Dr. Iraklis Klampanos serves as a Senior Lecturer in Data Systems and Data Engineering at the School of Computing Science, University of Glasgow. His academic role centers on advancing research and teaching in data-intensive computing systems within this prestigious institution. His research program spans Information Retrieval , Data Engineering , and Artificial Intelligence , with specialized expertise in peer-to-peer architectures, microblog search optimization, and generative AI interpretability. Key contributions include developing temporal relevance feedback mechanisms for social media search and evaluating distributed retrieval frameworks under realistic conditions. Analysis of his publication trajectory reveals a consistent evolution from foundational work on peer-to-peer testbeds (2003-2007) toward contemporary challenges in AI transparency. His recent 2025 publication on data influence analysis in generative models demonstrates ongoing innovation at the intersection of search technologies and explainable AI. Dr. Klampanos maintains active research collaborations, particularly with Joemon M. Jose, as evidenced by co-authorship across 15+ years of publications in major venues like TREC, TRECVID, and Lecture Notes in Computer Science.

Susan G. Campbellمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Human-Computer Interaction
• Data Privacy
• +۳ مورد دیگر

Susan G. Campbell is a Senior Lecturer at the University of Maryland, splitting her time between the College of Information Studies (iSchool) and the Applied Research Laboratory for Intelligence and Security (ARLIS). Her work focuses on cognitive skills for cybersecurity performance, human-technology interaction, and data privacy. She designs assessments for cyber aptitude and talent, with notable contributions to frameworks like Cyber Aptitude and Talent Assessment (CATA) for U.S. Special Operations Command (SOCOM). Her expertise spans cybersecurity education, cognitive engineering, and human-centered technology design. She leads projects such as Augmenting selection exercises for SOCOM and There is no “AI” in teams , exploring AI-human collaboration. Dr. Campbell’s research also addresses language aptitude measurement through tools like DLAB2 and Hi-LAB, emphasizing cognitive factors in second language acquisition. She is affiliated with the Organizational Teams & Technology Society (OTTRS) and Human-Computer Interaction Lab (HCIL), where she investigates sociotechnical systems and team cognition. Her work bridges cognitive science, information science, and applied research for real-world security challenges.

• Biomass Gasification
• Computational Fluid Dynamics
• Combustion Engineering
• +۵ مورد دیگر

Tian Li is an Adjunct Associate Professor at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Based at the Varmeteknisk building on the Gløshaugen campus, Dr. Li is affiliated with the ComKin Group and has been actively involved in numerous research projects focused on biomass conversion and combustion technologies since 2011. Dr. Li's research primarily focuses on: Biomass gasification and combustion technologies Computational Fluid Dynamics (CFD) modeling of energy conversion processes Multiphase flow and reaction kinetics in thermochemical processes Turbulence modeling in combustion systems Development of simulation tools for bioenergy applications Over the past decade, Dr. Li has led or contributed to multiple significant research projects funded by the Norwegian Research Council and industrial partners, including BioCarbUp, GASPRO, GrateCFD, GAFT, BioCarb+, CenBio, and GasBio. These projects have focused on optimizing biomass conversion processes for sustainable energy production. Dr. Li's publication record shows consistent contributions to high-impact journals in the energy and combustion fields, with a strong emphasis on computational modeling approaches. The research demonstrates expertise in developing and validating models for biomass conversion processes, with applications ranging from industrial-scale biomass furnaces to fundamental particle-level phenomena. Dr. Li has developed significant expertise in various computational tools and programming languages: Software: OpenFOAM, ANSYS Fluent, ANSYS ICEM CFD, Star-CD, MFiX, CHEMKIN, LOGEsoft, LabVIEW Programming: C/C++, Python, Fortran, Matlab Through participation in major research centers like CenBio (Bioenergy Innovation Centre), Dr. Li has contributed to advancing Norway's bioenergy research capabilities and fostering collaboration between academia and industry in the sustainable energy sector.

• Machine Learning operations (MLops)
• Data Science
• Software Engineering

Amal AKLI is a Researcher affiliated with the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), specifically within the Department of Computer Science. She is part of the Security Design and Validation research group (SERVAL), led by Prof. Yves Le Traon. Amal holds a Master’s degree in computer science from the National School of Computer Science in Algeria (2022). Her research focuses on Machine Learning operations (MLops), data science, and software engineering, with a particular emphasis on applying AutoML and few-shot learning techniques to improve software development and testing processes. Her recent publications explore parameter-efficient fine-tuning of pre-trained code models and predicting flaky tests using few-shot learning. While no scientific awards are explicitly mentioned, her work contributes to advancing automated testing and machine learning integration in software engineering. Amal's research aligns with the broader goals of the SERVAL team in enhancing software design and validation methodologies.

Brae Webbمشاهده پروفایل

مدرس

• Formal Methods
• Compiler Optimization
• Software Verification
• +۳ مورد دیگر

Brae Webb is an Associate Lecturer in the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. His email is b.webb@uq.edu.au . His research focuses on formal methods, compiler optimizations, and software verification, with contributions to verification frameworks for compiler optimizations and formal semantics of programming language implementations. He has co-authored multiple publications at conferences such as FormaliSE, CPP, and ICFEM, and contributed to editions of the textbook Software Architecture . His work spans theoretical and applied aspects of software engineering, including the use of proof assistants like Isabelle/HOL for verifying term graph optimizations. Recent projects include analyzing GraalVM's intermediate representation and differential testing methodologies for compiler verification frameworks. Brae Webb's publications reflect a commitment to advancing formal verification techniques in compiler design and software systems. His textbook series on software architecture emphasizes practical design principles for scalable software systems.