Find Academic Supervisors

Balázs CsontosView profile

Assistant Professor

• Web Accessibility
• Video Game Accessibility
• Pipeline Transportation Simulation
• +5 more

Dr. Balázs Csontos serves as an Assistant Professor at the Department of Computer Science within the Faculty of Information Technology at the University of Pannonia in Veszprém, Hungary. His office is located in Building I, room 915, and he can be contacted at +36 88 624 297 or csontos.balazs@mik.uni-pannon.hu. Dr. Csontos maintains a dual research focus that bridges human-computer interaction and industrial process optimization. His primary research interests include: Web accessibility, particularly for government websites and CMS-based systems Video game accessibility, with specific focus on Xbox console implementations Pipeline transportation simulation for oil industry applications Event-driven simulation methods for fuel transport in complex pipeline networks Supply chain optimization in petroleum industry contexts His publication record from 2018-2025 reveals a consistent research trajectory with two distinct but complementary strands. In accessibility research, he has published multiple studies in Universal Access in the Information Society examining Hungarian government websites and video game accessibility. Simultaneously, he has contributed to chemical engineering literature with sophisticated simulation models for pipeline transportation systems. This interdisciplinary approach connects computer science with practical applications in both public sector IT services and energy industry logistics. Dr. Csontos appears to be actively involved with the PIMS Academy, a postgraduate program run by the University of Pannonia in collaboration with MOL Group. This program focuses on educating specialists capable of optimizing oil industry value chains and multi-production site operations, leading to the Specialist of Supply Chain Optimization in Petroleum Industry (SCOPI) degree.

Xusheng XiaoView profile

Associate Professor

• Software Engineering
• Computer Security
• Program Analysis
• +9 more

Xusheng Xiao is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University, where he leads the Reliable, Intelligent, Secure, and Efficient (RISE) Software and System lab. Previously, he was an Assistant Professor at Case Western Reserve University (2017-2022) and a researcher at NEC Labs America (2014-2017). He received his Ph.D. in Computer Science from North Carolina State University in 2014 under the supervision of Prof. Tao Xie and Prof. Laurie Williams. Dr. Xiao's research spans software engineering and computer security, with emphasis on AI-enhanced software analysis approaches that combine software analysis and artificial intelligence to improve software reliability and security. His lab focuses on Large Language Model enhanced software analysis, mobile app analysis, cyber threat detection and investigation, blockchain/smart contract security, and software testing and debugging. His work has resulted in significant industry impact, including research integrated into NEC's security product (which won the Grand Prix award at CEATEC 2016) and a static analysis technique deployed at Microsoft Research that was granted a US patent. His recent publications reveal a strong trend toward applying Large Language Models to security challenges, with numerous papers on LLM-powered security analysis, mobile app security, and blockchain security. His work demonstrates a consistent focus on practical security solutions that address real-world threats while advancing theoretical foundations in program analysis. NSF CAREER Award NSF CRII Award Samsung GRO Award 2021 Case School of Engineering Faculty Research Award Grand Prix award at CEATEC Award 2016 Multiple US patents from industry collaborations Dr. Xiao has secured significant research funding from NSF, DOE-ARPA-E, and Samsung, and recently received API credits from the OpenAI Cybersecurity Grant Program and a grant from TensorBlock for collaboration on Forge. He actively mentors PhD students and has numerous advisees including Zichen Liu, Shang Ma, Liangyi Huang, Pengcheng Fang, and Fei Shao who have co-authored multiple publications with him. His RISE lab represents a comprehensive research ecosystem focused on developing advanced analysis techniques to analyze complex software behaviors for improving software reliability and security. The lab's work spans multiple security domains with particular emphasis on applying cutting-edge AI techniques to traditional security challenges.

Thiago FerreiraView profile

Assistant Professor

• Software Engineering
• Search-based Software Engineering
• Artificial Intelligence
• +4 more

Thiago Ferreira is an Assistant Professor in the College of Innovation & Technology at the University of Michigan-Flint, where he has been employed since October 2021. He holds a PhD in Computer Science from the Federal University of Paraná (2019) and a Master's from Ceará State University (2015). His research integrates optimization algorithms, artificial intelligence, and user preferences to solve software engineering challenges including requirements engineering, testing, and refactoring. Dr. Ferreira's education includes: PhD in Computer Science, Federal University of Paraná (2015-2019) Master's in Computer Science, Ceará State University (2013-2015) Bachelor's in Computer Science, Ceará State University (2007-2012) His research spans three core domains: Search-Based Software Engineering : Developing optimization frameworks like Nautilus for product line testing AI-Driven Refactoring : Creating intelligent change operators for code quality improvement Interdisciplinary Applications : Applying AI to assistive technology and STEM education equity projects Recent publications demonstrate a strong focus on Large Language Model applications in optimization, refactoring pattern analysis, and interdisciplinary STEM projects. His work consistently combines empirical validation with practical tool development. Awards and recognitions include: Hagerman Faculty Innovation Award (2024) Lois Matz Rosen Junior Faculty Excellence in Teaching Award Early Career Award (2023) Best Paper Award at SBCARS'24 Multiple Golden Apple Teaching Recognitions (2022-2024) He actively advises graduate students through thesis supervision (CSC 595/596) and independent studies. His Software Engineering and Artificial Intelligence Lab (SEAIL) focuses on AI-driven solutions for software engineering challenges. Current interdisciplinary collaborations include occupational therapy projects developing adaptive technologies.

Aymeric BlotView profile

Senior Lecturer

• Genetic Improvement
• Search-Based Software Engineering
• Algorithm Configuration
• +5 more

Aymeric Blot is a Senior Lecturer at the University of Rennes, France, where he is affiliated with the ISTIC faculty. He is also a member of the IRISA research centre within the joint Inria/IRISA DiverSE team. His work focuses on advancing software improvement techniques through genetic algorithms and automated optimization approaches, with significant contributions to the field of Genetic Improvement. Dr. Blot received his PhD in Computer Science from Université de Lille (2015-2018), following a Magistere in Computer Science and Telecommunications from ENS Rennes (2011-2015). His educational background also includes an MSc from ENS Cachan and a BSc from Université Rennes 1, establishing a strong foundation in both theoretical and applied computer science. Dr. Blot's research primarily centers on Genetic Improvement and Search-Based Software Engineering . His work explores how software can be automatically evolved and optimized through techniques like evolutionary computation, automated machine learning, and algorithm configuration. A key contribution to this field is his development and maintenance of Magpie , an automated software improvement framework. His research has practical applications in software performance optimization, program repair, and multi-objective combinatorial optimization problems. Dr. Blot has made significant contributions to understanding mutation operator efficacy, fitness function design, and the combination of different software improvement approaches. Analysis of Dr. Blot's recent publications shows a consistent focus on empirical evaluation of software improvement techniques. His work often compares different approaches like genetic improvement, parameter tuning, and their combinations to determine optimal strategies for software optimization. There's a clear trajectory toward more comprehensive surveys and frameworks that integrate multiple improvement techniques, as evidenced by his recent survey on benchmarks for software's non-functional properties published in ACM Computing Surveys. Dr. Blot has been actively involved in the academic community, serving on program committees for major conferences including ASE, ICSE, ISSTA, and GECCO. He has organized workshops on Genetic Improvement and contributed to community resources like the genetic improvement website. His service includes roles as Program Committee member, track chair, and keynote speaker at numerous international conferences. At the University of Rennes, Dr. Blot teaches Software Engineering and Operational Research courses. He previously served as a Research Associate at University College London (2018-2022) and at Université du Littoral Côte d'Opale (2022-2023) before joining his current tenured position in 2023. His international experience includes research visits to Leiden University in the Netherlands and Shinshu University in Japan.

Federica SarroView profile

Professor

• Software Engineering
• Search-Based Software Engineering
• Predictive Analytics
• +4 more

Federica Sarro is a Professor of Software Engineering at the Department of Computer Science, University College London (UCL), Faculty of Engineering Sciences. She leads the SOLAR (Software Optimisation, Learning and Analytics Research) group, focusing on cutting-edge research in software engineering methodologies and applications. Her research interests span Software Engineering , Predictive Analytics , Search-Based Software Engineering , and Empirical Software Engineering , with recent work emphasizing ethical considerations in modern software systems including fairness in AI, software sustainability, and responsible software engineering practices. Her keynote at Requirements Engineering 2023 highlighted how Search-Based Software Engineering can address ethical requirements in modern software systems, particularly around fairness, safety, and environmental considerations. Analysis of her recent publications reveals a strong focus on applying search-based and AI techniques to critical software engineering challenges, with increasing attention to ethical implications of software systems. Her work bridges theoretical advancements with practical industrial applications, particularly in automated program repair, fairness testing, and sustainable software development. As an active contributor to the software engineering community, Professor Sarro serves on steering committees for major conferences including SSBSE and SANER, and has held numerous leadership roles across ASE, ESEC/FSE, ICSE, and other premier software engineering venues. Her SOLAR research group continues to produce influential work at the intersection of software engineering and artificial intelligence.

Qingkai ShiView profile

Associate Professor

• Programming Languages
• Cybersecurity
• Software Engineering
• +3 more

Qingkai Shi is an Associate Professor in the School of Computer Science at Nanjing University, China, specializing in compiler techniques and formal program analysis for software and network security. His research bridges programming languages, cybersecurity, and software engineering with practical applications in real-world systems. Education: Ph.D. from Hong Kong University of Science and Technology Postdoctoral researcher at Purdue University, USA His research focuses on rigorous security validation through advanced static and dynamic analysis, particularly in network protocol parsers, compiler security, and autonomous systems. Key methodologies include context-sensitive program analysis, differential analysis, and formal specification techniques that address critical vulnerabilities in modern software ecosystems. Recent publications (2022-2026) demonstrate strong continuity in program analysis research with expanding applications to emerging domains like autonomous driving and Rust ecosystems. Work consistently targets high-impact security challenges in network protocols, compiler correctness, and memory safety, often yielding practical tools and industrial applications. Scientific Awards: Four ACM SIGPLAN/SIGSOFT Distinguished Paper Awards Google Research Paper Award Hong Kong Ph.D. Fellowship Dr. Shi co-founded Sourcebrella LLC (acquired by Ant Group), translating academic research into commercial security solutions. His ongoing industry collaborations demonstrate successful technology transfer from program analysis research. He leads an active research group developing open-source analysis tools including canary (alias analysis), netlifter (network protocol specification), and context-sensitive-reachability (data flow analysis), all available on GitHub with substantial community engagement.

Peng DiView profile

Adjunct Associate Professor

• Program Analysis
• Compiler
• Parallel Computing
• +5 more

Peng Di serves as an Adjunct Associate Professor at the University of New South Wales, Sydney while working as a Senior Staff Engineer and leading the Intelligent Platform Engineering team at Ant Group in Hangzhou, China. They also serve as an Industrial PhD Mentor at Zhejiang University. PhD from UNSW under supervision of Scientia Professor Jingling Xue Former Research Associate at Compiler Research Group Former Technical Expert of AI compiler at Huawei Peng Di's research spans the intersection of programming languages, artificial intelligence, and software engineering. Their work focuses on leveraging large language models for software development, program analysis techniques, compiler technologies, and security applications. They have pioneered approaches to integrate code large language models with traditional program analysis, creating hybrid systems that enhance software development efficiency and security. Their research in parallel computing and optimizations for heterogeneous systems has resulted in practical industrial applications, particularly in the domain of AI compilers and microservices architecture. Recent publications demonstrate a clear trend toward integrating graph-based representations with large language models for code understanding, advancing static analysis techniques using datalog and neural networks, and applying these innovations to practical industrial settings. Their work bridges theoretical program analysis with real-world software engineering challenges, particularly in microservices architectures and security-critical applications. T-Star Award (Ant Group's highest technological award, 2024) ACM SIGSOFT Distinguished Paper Award (2025) Distinguished Dissertation Award by the CCF Systems Software (2024) distinguished paper award of ASE'24 (2024) Peng Di leads the Intelligent Platform Engineering team at Ant Group, which has developed several significant open-source projects including CodeFuse (a pretrained code large language model), Ling-Coder-Lite (a MoE code LLM), and OpenDeRisk (an AI-native risk intelligence system). Their team collaborates with academic institutions including UNSW, East China Normal University, ETH Zurich, Chinese Academy of Sciences, and Nanjing University on cutting-edge research. Peng Di actively mentors PhD students as an Industrial PhD Mentor at Zhejiang University and recruits technical experts and interns for research in machine learning, program analysis, and AI software engineering. The Ant Intelligent Platform Engineering team, led by Peng Di, is committed to building a safe and trusted framework for intelligent, automated software engineering. They manage the CodeFuse AI Coding and DeRisk AIOps communities, which encompass pre-trained Code LLMs, agents, frameworks, toolchains, and databases. Their team has also contributed to agent development platforms Tbox and WeaveFox, with the latter being an AI frontend development platform launched by Ant Group.

Foutse KhomhView profile

Professor

• software maintenance and evolution
• cloud engineering
• machine learning systems engineering
• +3 more

Foutse Khomh is a Full Professor at Polytechnique Montréal, holding multiple prestigious chairs including Canada Research Chair Tier 1, Canada CIFAR AI Chair, and FRQ-IVADO Research Chair. He leads the SWAT Lab (Software Analysis and Testing) at Polytechnique Montréal and has established himself as a leading researcher in software engineering for AI systems. Dr. Khomh received his Ph.D. in Software Engineering from the University of Montreal in 2011. His research has been recognized with numerous awards including four ten-year Most Influential Paper Awards and eight Best/Distinguished Paper Awards. He was also awarded the CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019. His research focuses on software maintenance and evolution, cloud engineering, machine learning systems engineering, empirical software engineering, software analytics, and dependable and trustworthy AI/ML. Dr. Khomh has published over 200 conferences and journal papers, demonstrating his prolific contribution to the field of software engineering, particularly in the context of AI and machine learning systems. His recent publications show a strong emphasis on trustworthy AI, debugging deep learning systems, and addressing challenges in LLM-generated code. Four ten-year Most Influential Paper Awards Eight Best/Distinguished Paper Awards CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize (2019) Canada Research Chair Tier 1 Canada CIFAR AI Chair FRQ-IVADO Research Chair NSERC Arthur B. McDonald Fellow Dr. Khomh has mentored numerous students and researchers in the field, serving as a key advisor in the development of next-generation software engineering researchers. He has also secured significant research funding through his various chairs and fellowships. His leadership extends to major research initiatives including the DEpendable Explainable Learning (DEEL) project and the NSERC CREATE SE4AI training program, which focus on developing trustworthy AI systems and training future AI software engineers. He leads the SWAT Lab (Software Analysis and Testing Lab) at Polytechnique Montréal and is actively involved in the Confiance.ia Quebec initiative on Trustworthy AI, which aims to establish best practices for trustworthy AI development. His lab focuses on empirical studies of software systems, particularly those involving machine learning components, and develops tools and techniques for improving software quality and reliability.

Diomidis SpinellisView profile

Professor

• Software engineering
• IT security
• Computing systems
• +3 more

Diomidis Spinellis is Professor of Software Engineering in the Department of Management Science and Technology at the Athens University of Economics and Business, Greece, where he heads the Business Analytics Laboratory (BALab). He also holds a position as Professor of Software Analytics in the Department of Software Technology at the Delft University of Technology. His academic career spans multiple prestigious institutions and research domains. Spinellis's research interests center on software engineering, IT security, and computing systems. He has made significant contributions through his work on code quality, software tools, program comprehension, and debugging methodologies. His research bridges theoretical foundations with practical applications, evident in his development of widely-used open-source tools and his contributions to operating systems like Apple's macOS and BSD Unix. His scholarly output includes over 300 technical papers with more than 10,500 citations, demonstrating substantial impact across the software engineering community. His publications span diverse topics including software analytics, program comprehension, debugging techniques, and open-source software quality. Among his notable recognitions are two award-winning, widely-translated books: Code Reading and Code Quality: The Open Source Perspective , followed by Effective Debugging: 66 Specific Ways to Debug Software and Systems in 2016. His work has been influential in both academic and industry settings. IEEE Software editorial board member for a decade Editor-in-Chief of IEEE Software for four years Elected member of IEEE Computer Society Board of Governors (2013-2015) Senior member of ACM and IEEE Spinellis actively contributes to the software engineering community through tool development (CScout, UMLGraph, dgsh), conference leadership roles, and mentoring activities. His professional service includes program committee memberships across major software engineering conferences including ICSE, ESEC/FSE, and ASE.

Heng LiView profile

Assistant Professor

• Software Engineering
• Software Analytics
• Software Observability
• +7 more

Heng Li is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montreal, Canada. He leads the Measurement, Observation, and Optimization of Software and its Evolution (MOOSE) lab, focusing on software engineering research with emphasis on observability, log analysis, and performance engineering. His work bridges academic research with practical industry applications, drawing from his prior experience as a software engineer at Synopsys and BlackBerry. Education: Ph.D. in Computing from Queen's University, Canada M.Sc. from Fudan University, China B.Eng. from Sun Yat-sen University, China Heng Li's research spans multiple critical areas in modern software engineering. His primary focus is on software monitoring and observability , where he develops techniques to make software systems more transparent and understandable during operation. He has made significant contributions to software log mining , creating novel approaches for parsing and analyzing log data to detect anomalies and performance issues. His work in intelligent operations of software systems applies machine learning to automate various aspects of software operations. Additionally, he researches software performance engineering and mining software repositories to understand development practices and improve software quality. An analysis of Dr. Li's recent publications reveals a strong focus on practical applications of software analytics. His work consistently addresses real-world challenges in software observability, particularly log analysis and performance monitoring. A notable trend is his exploration of machine learning applications in software operations (AIOps), with increasing emphasis on efficient algorithms for log processing and anomaly detection. His research shows a progression from foundational techniques in log parsing to more sophisticated approaches for performance regression detection and privacy preservation in software logs. Many of his papers include empirical studies from industry settings, demonstrating his commitment to bridging the gap between academic research and industrial practice. Professional Service: Program Committee member for multiple top software engineering conferences including ASE, ICSE, ESEC/FSE Active participation in workshops and special tracks related to software analytics and AIOps Dr. Li leads the MOOSE (Measurement, Observation, and Optimization of Software and its Evolution) laboratory at Polytechnique Montreal. The lab focuses on developing innovative techniques for monitoring software systems, analyzing their behavior through logs and performance metrics, and optimizing their operation. Current projects in the lab include advanced log parsing algorithms, performance regression detection systems, and privacy-preserving techniques for software logs. The lab maintains strong connections with industry partners to ensure research relevance to real-world challenges.

Jacques KleinView profile

Professor

• Software Engineering
• Software Security
• Program Analysis
• +6 more

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.

Yuan TianView profile

Assistant Professor

• Software Engineering
• Applied Machine Learning
• AI for Software Engineering
• +7 more

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.

Bo WangView profile

Assistant Professor

• Compiler Testing and Tuning
• Mutation Analysis
• Automated Program Repair
• +3 more

Bo Wang is an Assistant Professor in the School of Computer Science and Technology at Beijing Jiaotong University (BJTU), where he serves as a Master's Supervisor. He received his PhD in Computer Science from Peking University in 2021, with prior educational background from University of Science and Technology of China (Master) and Central South University (Bachelor). His academic journey includes significant international experience, having been a visiting Ph.D. student at the National University of Singapore in 2019 under the supervision of Prof. Abhik Roychoudhury, and a visiting scholar at Singapore Management University in 2024. These experiences have contributed to his global perspective on software engineering research. Dr. Wang's research spans four primary areas of software engineering: Compiler Testing and Tuning - Focusing on identifying bugs in C++ compilers through innovative fuzzing techniques and type-driven mutation Mutation Analysis - Developing efficient approaches to mutation testing that reduce computational overhead while maintaining effectiveness Automated Program Repair - Creating systems that automatically fix software bugs, with emphasis on efficiency and validation techniques AI for Software Engineering - Exploring how large language models can enhance various software engineering tasks including fault localization and test generation His publication record demonstrates a consistent focus on practical software engineering problems with significant technical depth. Recent work shows an increasing integration of AI/ML techniques with traditional software engineering approaches, particularly in evaluating how large language models can contribute to automated program repair and fault localization. His research often targets efficiency improvements, seeking to make advanced software engineering techniques more practical for real-world application. Dr. Wang has been recognized with prestigious awards including the ACM SIGSOFT Distinguished Paper Award for his ISSTA 2017 work on mutation analysis and the IEEE TCSE Distinguished Paper Award for his ICSME 2025 paper on automated program repair. As an active member of the software engineering community, Dr. Wang serves on program committees for major conferences including ASE, FSE, and ICSE. He has advised multiple Master's students at Beijing Jiaotong University, guiding research in software testing, program analysis, and automated repair techniques. His work has been supported by academic grants that enable his research group to pursue innovative approaches to challenging software engineering problems. Dr. Wang maintains an active research group focused on software testing and analysis, with projects spanning compiler testing, mutation analysis, automated program repair, and AI applications in software engineering. His lab employs both traditional program analysis techniques and cutting-edge AI approaches to address longstanding challenges in software reliability and maintenance.

Yanyan JiangView profile

Associate Professor

• Software Engineering
• Software Testing
• Software Verification
• +2 more

Dr. Yanyan Jiang is an Associate Professor at Nanjing University, China, specializing in software systems with a focus on validation, verification, and synthesis. His research spans software testing, analysis, and program synthesis, with significant contributions to the field of software engineering. Dr. Jiang's research interests include: Software Testing - particularly code coverage validation and Android application testing Software Verification - with focus on compiler validation and system correctness Program Synthesis - especially for dynamic software updates and state transformation Operating Systems - including educational approaches and system design principles His recent work shows a strong trend toward practical applications of software engineering research, with focus on Android systems, compiler validation, and self-adaptive software. His publications demonstrate expertise in both theoretical approaches to software validation and practical tools for real-world systems, particularly in the areas of code coverage validation, dynamic software updating, and compiler verification. Dr. Jiang has received numerous prestigious awards: SOSP'23 Best Paper Award ACM SIGSOFT Distinguished Paper Awards (ICSE'21 and ASE'18) ACM Europe Council Best Paper Award Microsoft Research Asia Fellowship Award CCF Outstanding Doctoral Dissertation Award Dr. Jiang actively contributes to the software engineering community through program committee memberships at major conferences including ASE, ICSE, and ESEC/FSE. He also serves on the Steering Committee for regional Computing Olympiad contests and as a Technical Committee member for the Asia-Pacific Informatics Olympiad (APIO) 2022-2024, demonstrating his commitment to both research excellence and educational outreach in computer science.

Yijun YuView profile

Professor

• Requirements Engineering
• Automated Software Engineering
• Software Maintenance and Evolution
• +3 more

Yijun Yu is a Professor of Software Engineering at The Open University, UK, where he leads research on automated techniques to enhance software engineering productivity and software quality. He serves as Associate Editor for the Software Quality Journal and Chair of the BCS Specialist Group on Requirements Engineering, while actively contributing to program committees of premier conferences including ICSE, FSE, RE, ICSME, and SEAMS. His research spans Requirements Engineering, Automated Software Engineering, and Software Maintenance and Evolution, with emphasis on developing methods for software adaptation, security enhancement, and resilience engineering. Key contributions include techniques for requirements-driven adaptation, Rust-based safety mechanisms, and socio-technical resilience frameworks that bridge theoretical advances with practical industry applications. Analysis of his 2018-2024 publications reveals a strong trajectory in programming language safety (particularly Rust transpilation), neural code representation learning, and adaptive security systems. His work consistently addresses real-world challenges in memory safety, vulnerability prediction, and autonomous system behavior through innovative static analysis, machine learning, and formal methods approaches. His scientific achievements include: 10 Year Most Influential Paper award (CASCON’16) 6 Best Paper awards (SEAMS’18, iRENIC’16, TrustCom’14, EICS’13, VMPDP’01) 3 Distinguished Paper awards (RE’11, BCS’08, ASE’07) Best Tool Demo Paper Award (RE’13) Best Student Paper Award (PDCS’02) As Principal Investigator, Professor Yu has managed knowledge transfer projects with industry leaders: NATS (air traffic management) Huawei (telecommunications) IBM (enterprise software) CA Technologies (IT management) RealTelekom (network solutions) These collaborations focus on translating academic research into practical solutions for requirements engineering, adaptation techniques, and security challenges in industrial software systems.