جستجوی استاد راهنمای دانشگاهی

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

Raghavan Komondoor is an Associate Professor in the Department of Computer Science and Automation at the Indian Institute of Science (IISc), Bangalore. His research focuses on programming languages, program analysis, and software engineering, with a strong emphasis on developing automated tools that help programmers understand, verify, and transform programs quickly and reliably. His primary research interests include: Programming languages and program analysis Software verification and testing Static analysis techniques Null dereference verification Points-to analysis Web application analysis ORM-based controller verification Memory optimization in Java programs Code clone detection and elimination Dr. Komondoor has developed several influential research tools including NPEDetector for null dereference verification, Ross for verifying Java program safety, DFAS for static analysis of asynchronous systems, PageModeler for web application analysis, and ORMInfer for verification of ORM-based controllers. His research spans over a decade with publications in top-tier software engineering conferences including ASE, ICSE, ISSTA, and SPLASH. His recent publications demonstrate a consistent focus on formal methods and program verification, with significant contributions to symbolic fixpoint algorithms, ORM controller verification, and controller synthesis over infinite state spaces. These works build upon his earlier foundational research in points-to analysis, web application analysis, and null pointer verification. Dr. Komondoor's service contributions to the academic community include: Program Committee Co-Chair for ATVA 2025 Organizing co-chair for ISEC 2024 Chair of PhD Symposium committee for ISEC 2020 Program committee membership for ICSE (2018-2025), ASE (2019-2020), ISSTA (2022), and numerous other major conferences He has successfully mentored numerous PhD and M.Tech students, many of whom now work at leading organizations including Microsoft, Siemens Research, Goldman Sachs, and academic institutions. Currently, he advises multiple PhD students and M.Tech researchers in the Programming Languages Laboratory at IISc. He teaches courses on Program Analysis and Verification, Principles of Distributed Software, and Formal Methods in Software Engineering, and maintains active research projects with open positions for research staff.

• Programming languages
• Program analysis
• Software engineering
• +۹ مورد دیگر

Guangjie Li is a researcher at the National Innovation Institute of Defense Technology, actively contributing to software engineering research through publications in premier conferences including ASE, ESEC/FSE, and SANER. His work bridges theoretical program analysis with practical machine learning applications for software development challenges. His research focuses on: Fault localization techniques enhanced by statement-level error analysis Deep learning-driven detection of code smells like feature envy Automated extraction of program elements across software versions Empirical validation using real-world codebases Integration of version control data in program analysis Recent publications demonstrate a consistent trajectory toward machine learning augmentation of traditional software engineering tasks, particularly in debugging and code quality assessment. His methodologies emphasize practical applicability through real-world example integration, addressing critical gaps in automated software maintenance.

• Software Engineering
• Program Analysis
• Fault Localization
• +۲ مورد دیگر

Stefano Zacchiroli is a Full Professor of Computer Science at Télécom Paris, part of the Polytechnic Institute of Paris. He is affiliated with the Laboratory of Information Processing and Communication (LTCI) where he conducts research on digital commons, open source software engineering, computer security, and the software supply chain. As a member of the ACES team, he focuses on critical aspects of software preservation and development. His research spans several interconnected domains in software engineering. His work on digital commons explores how to preserve and make accessible the collective knowledge of software development. His research on open source software engineering investigates the processes, tools, and social dynamics that make open collaboration successful. In computer security, he examines vulnerabilities in the software supply chain, while his work on formal methods applies mathematical rigor to software development challenges. His leadership in Software Heritage demonstrates his commitment to preserving software as a crucial part of our digital heritage. His recent publications reveal a strong focus on software preservation, repository analysis, and security in the software supply chain. There's a clear trend toward understanding how software evolves over time, how to verify its integrity, and how to ensure reproducible builds. His work bridges theoretical computer science with practical software engineering challenges, particularly in the context of open source ecosystems. His scientific recognition includes: 2015 O'Reilly Open Source Award 2022 Google Award for Inclusion Research As an academic advisor, Zacchiroli has likely mentored numerous students through his position at Télécom Paris. His involvement in numerous conference program committees demonstrates his active role in the academic community. His research has been supported by institutional affiliations and likely various grants related to software preservation and open source research. Zacchiroli leads or contributes to several important initiatives, most notably Software Heritage, which serves as the largest public archive of software source code. His work with the Debian project and the Open Source Initiative demonstrates his commitment to practical applications of his research. The ACES team at LTCI provides the academic home for much of his current research, focusing on critical aspects of software engineering and preservation.

• Software Engineering
• Open Source Software
• Free Software
• +۵ مورد دیگر

Sang Kil Cha is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology) where he holds positions in both the Graduate School of Information Security and the School of Computing. He serves as the director of the Cyber Security Research Center (CSRC) at KAIST and leads the SoftSec Lab. Dr. Cha received his Ph.D. and M.S. degrees from Carnegie Mellon University and his B.S. degree from Korea University. Current Position: Associate Professor, KAIST Leadership: Director of Cyber Security Research Center (CSRC) Laboratory: Head of SoftSec Lab Education: Ph.D. and M.S. from CMU, B.S. from Korea University Dr. Cha's research focuses on the intersection of computer security and software engineering, with particular emphasis on building and evaluating systems that can analyze programs. His work spans software security, software engineering, software systems, and program analysis. He has made significant contributions to binary code analysis, fuzzing techniques, and reverse engineering. His research has practical applications in vulnerability detection, malware analysis, and secure software development. His publication record demonstrates consistent high-impact contributions to the field, with numerous papers in top-tier security and software engineering conferences including IEEE S&P, USENIX Security, ISSTA, and ICSE. His recent work shows a continued focus on advancing fuzzing methodologies, binary analysis techniques, and security applications for blockchain technologies. Dr. Cha's research group has produced influential tools such as B2R2 (a binary analysis framework) and ofuzz (a fuzzing framework). ACM Distinguished Paper Award USENIX Distinguished Paper Award Best Paper Award NDSS Best Paper Award As an educator, Dr. Cha has taught courses including Binary Code Analysis and Secure Software Systems, Advanced Software Security, and Introduction to Information Security. His research group has mentored numerous students who have become co-authors on his publications. His work is supported by various research grants focused on software security and analysis techniques. The SoftSec Lab maintains active collaborations with both academic and industry partners in the security research community.

• Software Security
• Software Engineering
• Program Analysis
• +۴ مورد دیگر

Amin Milani Fard is an Associate Professor of Computer Science at New York Institute of Technology - Vancouver Campus, and a visiting faculty member in Management Information Systems at Simon Fraser University's Beedie School of Business in Vancouver, Canada. Previously, he served as an Assistant Professor at NYIT Vancouver from 2018 to 2023. Dr. Milani Fard received his Ph.D. in Computer Software Engineering from the University of British Columbia (2012-2017), his M.Sc. in Computer Science from Simon Fraser University (2009-2010), and his B.Sc. in Computer Software Engineering from Ferdowsi University of Mashhad. His academic journey began with notable achievements including the 1st Rank Khwarizmi Award (awarded by Iran's President Mohammad Khatami) and an Exceptional Talents Admission Award. His research spans multiple disciplines, primarily focusing on Security, Privacy, and Assurance of Software and Information , with significant contributions to software testing, blockchain security, financial market prediction, and machine learning applications. His work on JavaScript security code smells, Ethereum smart contract vulnerabilities, and financial market prediction using multimodal data has been widely recognized. His publications demonstrate a consistent pattern of high-quality research with numerous papers in top conferences including ASE, ICST, and IEEE journals. Dr. Milani Fard's research portfolio shows a clear evolution from foundational work in software testing and JavaScript analysis toward more complex applications in financial technology and AI-driven security solutions. His most recent work focuses on integrating LLM technologies with security applications and advancing financial prediction models using sophisticated time series analysis. Scientific Awards and Recognition: Most Influential Paper Award at IEEE SCAM 2023 Presidential Excellence Award Finalist for Teaching at NYIT (2023) Best Paper Award at ECIR 2019 Multiple research grants including NSERC Alexander Graham Bell Canada Graduate Scholarship IEEE Best Paper Award Nominee at ICST 2017 As an active researcher, Dr. Milani Fard serves on program committees for major conferences including ASE and has contributed significantly to the software engineering community through his publications and research leadership. His work bridges theoretical computer science with practical applications in cybersecurity and financial technology, demonstrating both academic rigor and real-world impact.

• Software Engineering
• Cybersecurity
• Software Testing
• +۵ مورد دیگر

Prof. Dr. Jacques Klein is a Chief Scientist (Full Professor) and Co-head of the TruX Research Group at the SnT Centre (Security and Trust Centre) of the University of Luxembourg. With a career spanning over 15 years at the university, he has progressed from Research Scientist (2010-2015) to Senior Research Scientist (2015-2019), Associate Professor (2019-2022), and finally to Full Professor in 2023. His research focuses on three main areas: program analysis applied to mobile security, software debugging (particularly bug localization and program repair), and NLP/AI for software engineering. He has supervised numerous PhD students and has acquired over 5 million euros in research funding through projects from the Luxembourg Research Agency, European Commission, and industrial partnerships. Prof. Klein is an active member of the software engineering research community, serving as General Co-Chair of ASE 2023 and MobileSoft 2025, and as a steering committee member for ISSTA, ASE, and MobileSoft. He is also an Editorial Board Member of the Springer Empirical Software Engineering Journal since 2021. His recent publications demonstrate a strong focus on applying AI and machine learning techniques to software security challenges, particularly in the Android ecosystem, with significant contributions to malware detection, code analysis, and privacy compliance. His work has been recognized with multiple prestigious awards including the ICSE 10-year most influential paper award and the PLDI Most Influential Paper Award. Member of the Institut Grand-Ducal - Section des Sciences (since June 2024) Principal Investigator on numerous research projects Extensive service on program committees for major software engineering conferences He teaches courses on static program analysis, software vulnerabilities, and security analysis at the Master's level, and has made significant contributions to the Android research community through the AndroZoo dataset containing over 24 million Android applications.

• Software Engineering
• Software Security
• Program Analysis
• +۶ مورد دیگر

Dr. Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Canada. Her research focuses on applying artificial intelligence and machine learning techniques to solve software engineering challenges, particularly in the areas of code analysis, technical debt management, and developer productivity enhancement. Dr. Tian received her Ph.D. in Information Systems from Singapore Management University in May 2017 under the supervision of Prof. David Lo (IEEE/ACM fellow). Prior to joining Queen's University, she worked as a data scientist at the Living Analytics Research Centre (LARC) in Singapore. She has also conducted research visits at Carnegie Mellon University in 2015 and Inria Paris in 2013. Dr. Tian's research spans several key areas in software engineering with AI: Automatic technical debt, bug, and code change management LLM applications for code transformation and generation Human-AI collaboration in software development Analysis of developer interactions with AI tools like ChatGPT Mining software repositories for insights into development practices Her recent work has increasingly focused on leveraging Large Language Models to address software engineering challenges, with publications examining code translation, technical debt identification, and the dynamics of developer-AI interactions. Her research demonstrates a strong empirical approach, often analyzing large datasets from GitHub and other software development platforms. Dr. Tian has received recognition for her work, including the Best Research Paper Award at AI Foundation Models and Software Engineering (Forge), 2024 for her paper "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation." Dr. Tian leads the RISE research lab at Queen's University, which currently includes 5 PhD students, 2 MSc students, and 2 undergraduate research assistants. She has successfully supervised several graduate students to completion, with alumni now working at institutions including Duke University and Veeva Systems. Her research is supported by funding including an NSERC Alliance-Mitacs project on "Pragmatic Automated Code Transformation Leveraging Large Language Models" in collaboration with industry partner Ross Video. The RISE lab (Goodwin 621) is dedicated to developing reliable and intelligent support for software engineering. The lab's current research focuses on three main thrusts: automatic technical debt/bug/code change management, LLM for code transformation, and human-AI collaboration in software development.

• Software Engineering
• Applied Machine Learning
• AI for Software Engineering
• +۷ مورد دیگر

Yakun Zhang is an Associate Professor at the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). She holds a Ph.D. in Software Engineering from Peking University (2021-2025) and was a visiting scholar at the National University of Singapore. Her research integrates AI technologies like large language models to advance software engineering automation. Education Ph.D. in Software Engineering, Peking University (2021-2025) M.S. in Software Engineering, University of Chinese Academy of Sciences (2018-2021) B.S. in Computer Science, Wuhan University (2014-2018) Research Focus Her work spans: Intelligent Software Engineering (LLM-powered code generation/testing), Multi-Agent Systems (collaborative AI agents), and Trustworthy LLMs (addressing hallucinations/security). Recent publications demonstrate strong emphasis on GUI testing automation and dynamic analysis frameworks. Awards & Honors CCF System Software Outstanding PhD Dissertation Nomination (2025) Tencent Qingyun Talent Program & ByteDance Soaring Star Talent (2025) Outstanding Graduate of Beijing Municipality & Peking University (2025) National Scholarship & President's Awards (Peking University/CAS) Academic Service PC member for ASE 2025, ICSE 2026, and MSR 2025. Reviewer for ACM TOSEM and IEEE TSE. Actively recruits PhD/Master's students and collaborates with NUS, Microsoft, and Tencent.

• Intelligent Software Engineering
• Multi-Agent Systems
• Trustworthy Large Language Models
• +۱ مورد دیگر

Fatemeh Hendijani Fard is an Assistant Professor in the Department of Computer Science at the University of British Columbia's Okanagan campus. She serves as a graduate student supervisor and teaches courses in Computer Science and Data Science. Dr. Fard is a member of the CITECH program and MMRI, part of the Killam family of scholars, and an active member of both IEEE and ACM. Her research focuses on the intersection of Natural Language Processing and Software Engineering, with particular emphasis on developing code intelligence models for low-resource programming languages like R. She conducts empirical studies and develops techniques to improve the computational efficiency of code-language models while making them accessible to communities with restricted GPU access. Her work strongly advocates for Diversity and Inclusion in STEM, particularly for underrepresented females. Analysis of Dr. Fard's recent publications reveals a strong research trajectory in adapting Large Language Models for code intelligence with a focus on efficiency and accessibility. Her work spans multiple dimensions including code summarization, method name prediction, code search, code clone detection, and program repair, with special attention to low-resource programming languages. A notable trend is her exploration of adapter-based approaches for knowledge transfer that reduce computational requirements while maintaining performance. Izaak Walton Killam Memorial Scholarship Alberta Innovates Technology Futures (AITF) NSERC Discovery NSERC CREATE Mitacs Accelerate UBC Start-up Fund Dr. Fard has secured significant research funding including NSERC Discovery, NSERC CREATE, Mitacs Accelerate, and UBC Start-up funds to support her work on code intelligence for low-resource programming languages. She actively serves as a graduate student supervisor, guiding research in areas related to code representation learning and mining software repositories. Her service to the academic community is extensive, having served on program committees for major conferences including FSE, MSR, ASE, SANER, and ICSME across multiple years. Dr. Fard leads research initiatives focused on making code intelligence accessible to communities working with understudied programming languages. Her team conducts empirical studies and develops new techniques specifically designed for low-resource languages, with particular attention to the R programming language. This work addresses diversity and inclusion in AI tools by ensuring developers with limited computational resources can benefit from advances in neural networks and automated tools.

• Code Intelligence
• Transfer Learning
• AI4SE
• +۵ مورد دیگر

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

• Software Engineering
• Software Maintenance/Evolution
• Program Comprehension
• +۴ مورد دیگر

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

• Secure Software Engineering
• Static Program Analysis
• Automated Program Analysis
• +۵ مورد دیگر

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

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +۷ مورد دیگر

Yun Lin is an Associate Professor and Deputy Head of the Department of Computer Science and Technology at Shanghai Jiao Tong University's School of Computer Science. Prior to joining SJTU, Lin served as a Research Assistant Professor at the National University of Singapore working with Prof. Dong Jin Song. Lin leads the CoPhi ("Code Philia") research group, which focuses on the intersection of Software Engineering, AI, and Security. Lin's research spans three major areas: Automatic Programming (including code editing, software testing, and debugging), Explainable AI (focusing on representation interpretation and training data attribution), and Web Misinformation (particularly phishing and scam detection). The research has resulted in numerous tools including CoEdPilot for code editing recommendation, DeepDebugger for interactive debugging of deep classifiers, and Phishpedia for phishing webpage detection. Lin's recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models and vision language models, with traditional software engineering and security tasks. The work shows increasing sophistication in understanding project context, handling interactive nature of programming tasks, and addressing security challenges in the age of generative AI. Key themes include consistency-based approaches for anomaly detection, agent-based frameworks for complex tasks, and hybrid models that combine symbolic reasoning with neural approaches. ACM Distinguished Paper Award in ICSE'18 for "Towards Optimal Concolic Testing" Distinguished Reviewer Award in FSE'25 2nd prize Research Prototype Award in ChinaSoft'24 Lin advises a large team of PhD, Master's, and undergraduate students, with several publications co-authored with students appearing in top venues. Current research is supported by collaborations with National University of Singapore, particularly with Prof. Dong Jin Song, and includes projects on code editing, GUI testing, and phishing detection. The CoPhi group maintains active development of multiple research tools and datasets. The CoPhi research group under Lin's leadership focuses on building practical tools that bridge the gap between theoretical advances and real-world programming and security challenges. The group's work spans from fundamental program analysis techniques to applied security solutions, with an increasing emphasis on leveraging AI capabilities while maintaining explainability and reliability.

• Automatic programming
• Software debugging
• Software testing
• +۸ مورد دیگر

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

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +۵ مورد دیگر