Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.
Chao Peng is a Principal Research Scientist at ByteDance where he leads the Software Engineering Lab, focusing on AI agents for software engineering. He holds a part-time position as a Postgraduate Student Mentor at Fudan University's School of Computer Science. His research bridges industry and academia, with significant contributions to software testing, program repair, and LLM applications in software development. Education: PhD in Informatics, 2021, University of Edinburgh, UK MSc in High Performance Computing and Data Science, 2017, University of Edinburgh, UK BEng in Computer Science and Technology, 2016, Xuzhou University of Technology, China Dr. Peng's research interests center on the intersection of artificial intelligence and software engineering. He explores how large language models can transform traditional software development practices, particularly in code generation, testing, and bug fixing. His work on LLM4Code has led to innovative frameworks like CodeVisionary for evaluating code generation capabilities and Trae Agent for software engineering tasks with test-time scaling. He investigates the synergy between machine learning techniques and compiler optimizations to enhance software reliability and developer productivity. His recent publications reveal a strong focus on practical evaluation frameworks for LLMs in real-world software engineering contexts. Rather than theoretical benchmarks, his work emphasizes real-world applicability, as seen in RepoMasterEval which evaluates code completion in actual repository settings. He examines multi-faceted challenges including code generation, bug reproduction, issue resolution, and repository-level question answering, consistently addressing the gap between laboratory evaluations and practical development environments. Scientific Awards: Distinguished Reviewer for FSE'25 Invited to program committees for FSE'26, SANER 2026, ASE 2025, and others School of Informatics Scholarship (fully-funded PhD) Multiple national scholarships during undergraduate studies Honours Spot Bonus at ByteDance Dr. Peng actively mentors postgraduate students at Fudan University while leading research initiatives at ByteDance that foster university collaborations. His laboratory work translates academic research into practical tools for software development, with several frameworks deployed in industrial settings. He serves on multiple conference program committees, contributing to the advancement of software engineering research through rigorous peer review and community building. His Software Engineering Lab at ByteDance operates at the forefront of AI-assisted development, exploring how agent-based systems can automate complex software engineering tasks. The team's work on frameworks like AEGIS for bug reproduction and DialogAgent for code question answering demonstrates their commitment to solving practical challenges faced by developers in real-world settings.
Zhenyu Chen is a Full Professor and Director of the iSE Laboratory at Nanjing University, specializing in AI-driven software testing methodologies. His research bridges artificial intelligence and software engineering with dual focus areas: leveraging AI to enhance testing processes ( AI for Testing ) and validating AI/ML systems ( Testing for AI ). His research interests center on deep learning framework testing , crowdsourced testing optimization , and Large Language Model applications in verification . Recent work demonstrates innovative approaches to metamorphic testing of neural networks, LLM-based test report analysis, and security hardening of code models against backdoors. Key contributions include the development of mooctest.com and frameworks like DevMuT for mutation testing of deep learning APIs. His publication trajectory reveals evolving focus from crowdsourced testing (2018-2020) to deep learning system validation (2021-2023) and current emphasis on LLM-powered testing solutions. Major venues include ASE, ICSE, and ISSTA where he serves regularly on program committees.
Dr. Jifeng Xuan is a Professor and Deputy Dean at the School of Computer Science, Wuhan University, China. He founded the CSTAR (Centre of Software Testing, Analysis and Reliability) and holds editorial roles at Empirical Software Engineering and PLOS One . Previously, he was a postdoctoral researcher at INRIA Lille-Nord Europe (France) and earned his PhD from Dalian University of Technology. Research Interests: His work focuses on software testing, debugging, automated program repair, software data analysis, and search-based software engineering. He integrates AI/ML techniques for tasks like log analysis, fuzz testing, and vulnerability detection, with applications in robotics, microservices, and Android development. Publication Trends: Recent articles (2022–2025) emphasize AI-driven software engineering, including LLM-based repair, reinforcement learning for testing, and deep learning surveys. Security (vulnerability logs) and empirical studies on industrial challenges (e.g., C program repair) are recurring themes. Awards & Honors: ACM SIGSOFT Distinguished Paper Award (2025) IEEE TCSE Distinguished Paper Award (2025) CCF NASAC Youth Software Innovation Award (2024) Outstanding Doctoral Dissertation Award, China Computer Federation (2014) Luojia Young Scholar (2015) Student Advising & Labs: Actively recruits PhD and master students for CSTAR Lab. Research areas include automated debugging, testing tools (e.g., Mergebot, FastLog), and AI-generated code assessment. No specific grants listed.
Prof. Tegawendé F. Bissyandé is a Chief Scientist in the Professor category at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. He holds the prestigious position of ERC Fellow and serves as Principal Investigator of the NATURAL project focused on Artificial Intelligence for Program Repair. His research spans software engineering, cybersecurity, and artificial intelligence, with particular emphasis on applying machine learning techniques to software development and security challenges. Dr. Bissyandé's research interests include: Software Debugging (especially bug localization and program repair) Software Security (especially malware detection and analysis) Code Search (both free-form and semantic code-to-code) Machine Learning and Natural Language Processing for software engineering Cyber-security applications in mobile and cloud environments His recent work demonstrates a strong focus on leveraging Large Language Models (LLMs) for various software engineering tasks. Analysis of his 15 most recent publications reveals several key trends: extensive application of LLMs to program repair and code generation; innovative approaches to Android security and malware detection; development of novel techniques for code search and understanding; and exploration of the intersection between natural language processing and software engineering. His research increasingly bridges theoretical software engineering with practical applications in mobile security and developer productivity tools, with a significant portion of his work focusing on Android ecosystem security and program repair technologies. Dr. Bissyandé has received numerous prestigious awards throughout his career: APSEC Best ERA Paper Award (2018) for 'LSRepair: Live Search of Fix Ingredients for Automated Program Repair' IPSJ SIG SE Excellent Research Award (2018) for 'FaCOY: a Code-to-Code Search Engine' FOSS Impact Paper Award (2018) for 'Characterizing Deprecated Android APIs' SANER Best ERA Paper Award (2016) for 'Parameter Values of Android APIs: A Preliminary Study on 100,000 Apps' ASE Best Paper Award (2012) for 'Diagnosys: automatic generation of a debugging interface to the Linux kernel' As an active member of the software engineering research community, Dr. Bissyandé serves on program committees for major conferences including ICSE, ASE, and ISSTA, and has been an Area Chair for ICSE 2024. His industry partnerships include significant collaborations with BGL BNP Paribas (since January 2019), Luxembourg Stock Exchange (since January 2018), and Paul Wurth (January 2015 to 2018), demonstrating the practical impact of his research. He leads the SerVAL lab at SnT, which focuses on software validation and analysis, with particular expertise in mobile security and program repair technologies, and actively mentors PhD candidates through FNR research grants.
Andreea Costea is an Assistant Professor in the Programming Languages Group within the Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS) at Delft University of Technology. She joined TU Delft in October 2024 after completing her PhD at the School of Computing, National University of Singapore (NUS), where she worked in the Programming Languages and Software Engineering lab collaborating with the Automated Program Repair team, Trustworthy and Secure Software group, and VERSE lab. Her primary research focuses on programming languages design and implementation, with particular emphasis on software verification for critical code, program synthesis, and automated program repair. She maintains strong connections with industry while pursuing formal methods research, especially in the context of Rust programming language safety and interoperability. Dr. Costea's publication record demonstrates consistent contributions to software engineering and programming languages research, with recent work focusing on automated program repair techniques, Rust language safety mechanisms, and communication protocol verification. Her research shows a clear trajectory from theoretical foundations in session types and separation logic toward practical applications in memory safety and program repair. She actively serves the research community as Program Committee member for major conferences including ASE, ICSE, ICFP, and APLAS. Her service includes chairing publicity committees for SPLASH and artifact evaluation for ESOP. Regular Journal Reviewer: CACM, TOSEM, TSE Panel discussions: PLMW @ POPL'22, PLDI'21, PLMW @ PLDI'21, POPL'21 Extensive reviewing for top-tier conferences including POPL, OOPSLA, CAV, VMCAI Dr. Costea supervises multiple Master's students working on Rust-related safety projects and is actively recruiting PhD students to work on software interoperability, particularly focusing on how to restore Rust's safety guarantees when integrating with legacy C code and ensuring correct interaction between components written in different languages.
Sam Malek is a Professor in the Department of Informatics at the University of California, Irvine's School of Information and Computer Sciences, where he directs the Software Engineering and Analysis Laboratory. Previously serving as Director of the Institute for Software Research (2018-2022), his academic leadership spans software engineering research and institutional management. His research centers on software engineering with specialized expertise in software analysis and testing , mobile computing , security , software architecture , and accessible computing . Malek's work consistently focuses on developing practical techniques and tools for constructing, analyzing, and maintaining large-scale software systems, with recent emphasis on accessibility challenges in mobile and web environments. His publication trends reveal a strategic shift toward accessible computing since 2020, where he pioneered frameworks like Ma11y for web accessibility testing and Groundhog for mobile app crawling. Earlier work established foundations in energy testing (2020), GUI input generation (2021), and architectural analysis, demonstrating methodological evolution from core software engineering to human-centered accessibility solutions. ACM SIGSOFT test-of-time award (2020) National Science Foundation CAREER award (2013) GMU Emerging Researcher/Scholar/Creator award (2013) GMU Computer Science Department Outstanding Faculty Research Award (2011) Malek serves on editorial boards for ACM Transactions on Software Engineering and Methodology and ACM Transactions on Autonomous and Adaptive Systems , while frequently acting as a software expert witness in intellectual property litigation. His research group actively recruits PhD students through UCI's Software Engineering PhD program, with current projects focusing on AI-driven accessibility solutions and mobile testing frameworks. The Software Engineering and Analysis Laboratory under his direction maintains strong industry connections through sponsored research and tool development, particularly in accessibility testing and mobile application quality assurance.
Roopsha Samanta is an Assistant Professor in the Department of Computer Science at Purdue University, where she leads the Purdue Formal Methods (PurForM) research group and is a core member of the Purdue Programming Languages (PurPL) group. She completed her PhD at the University of Texas at Austin in 2013 under the supervision of E. Allen Emerson and Vijay K. Garg, followed by postdoctoral research at IST Austria (2014-2016) with Thomas A. Henzinger. Her research focuses on developing foundational techniques at the intersection of formal methods and programming languages, with primary interests in: Program synthesis and repair using semantic guidance Modular verification of distributed systems Concurrency and synchronization synthesis Robustness analysis of I/O systems Her publication record shows consistent contributions across programming languages and formal methods venues, with recent emphasis on explainable program synthesis, bounded verification of distributed systems, and semantics-guided approaches to enhance synthesis robustness. Her work frequently appears in premier conferences including PLDI, POPL, OOPSLA, and CAV. Notable scientific recognitions include: NSF CAREER Award (2019) Amazon Research Award (2021) She actively advises graduate and undergraduate researchers in the PurForM group, with current students including Nouraldin Jaber, Christopher Wagner, and Yongwei Yuan. Her research is supported by grants from NSF and Amazon. Beyond research, she teaches courses on program reasoning (CS560) and neurosymbolic program synthesis (CS592), and serves on steering committees for VMW@CAV and DARS. She also co-edits the ACM SIGPLAN Blog on PL Perspectives.
Kangwei Xu is a researcher at the Chair of Design Automation (Lehrstuhl für Entwurfsautomatisierung) under Prof. Ulf Schlichtmann at the Technical University of Munich (TUM), actively advancing Electronic Design Automation through AI-driven methodologies. His core research interests include: Electronic Design Automation (EDA) High-Level Synthesis Machine Learning for EDA Neural Network Accelerators Timing Analysis Hardware Reliability Analysis of his 2024-2025 publications reveals a decisive trend in leveraging Large Language Models to revolutionize hardware design flows. Key innovations span automated C/C++ code refactoring (HLSRewriter), behavioral discrepancy testing (HLSTester), and neural network logic optimization, demonstrating how AI integration significantly enhances efficiency and accuracy in EDA toolchains while addressing longstanding challenges in synthesis and verification. Scientific Awards: No awards documented in available sources. Advising and Grants: Public records indicate no formal student advisement roles or individually attributed research grants; his work operates within the broader funded projects of TUM's Design Automation chair. Labs and Teams: Xu contributes to TUM's interdisciplinary Design Automation research group, which spans Analog EDA, Electronic System Level design, Emerging Technologies, Microfluidics, Optical NoC, Novel Microfabrication, Timing Analysis, Neural Networks and Accelerators, and Reliability, maintaining strong industry collaborations and cutting-edge experimental facilities.
Thomas Lemberger is a researcher in the Department of Computer Science at Ludwig-Maximilians-Universität München (LMU Munich), contributing to the Software and Computational Systems Lab. He specializes in software verification, formal methods, and automated testing, with a focus on improving tool efficiency and scalability. His work includes extensions to CPAchecker, such as distributed summary synthesis and cooperative verification approaches, as well as developing user-friendly tools like CoVeriTeam GUI. His research interests involve integrating verification into build systems and IDEs, optimizing verification workflows, and exploring hybrid techniques that combine testing and formal methods. He has actively participated in competitions like SV-COMP and Test-Comp, contributing tools like PRTest and Nacpa. His projects aim to reduce tool restarts, enhance fault localization, and streamline verification processes through parallel portfolio analyses. Thomas mentors students on topics related to verification tool development, test-case generation, and open-source software. His contributions are supported by grants from the DFG (CONVEY, COOP, IDEFIX), emphasizing cooperative verification and scalable analysis. Recent work focuses on enabling developers to use verification tools seamlessly within their existing workflows.
Lingling Fan is an Associate Professor (100 Young Academic Leaders of Nankai University) at Nankai University, China. Her research focuses on software security analysis, software testing and analysis, and big data-driven analysis, with significant contributions to mobile application security, particularly in Android security and accessibility. Her research interests include: Software Security Analysis, with emphasis on mobile application security and vulnerability detection Software Testing and Analysis, particularly for Android applications and accessibility issues Big Data-driven Analysis for security and quality assessment of software systems Dr. Fan's publication record shows strong trends in automated security testing, vulnerability detection in open-source ecosystems, and accessibility analysis for mobile applications. Her work spans multiple disciplines including software engineering, security, and human-computer interaction, with a particular focus on practical applications for Android ecosystem and measurable real-world impact. Her notable scientific awards include: ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICSE 2018) Research Tool Award at NASAC 2018 National Scholarship from The Ministry of Education, China (2018) ACM SIGSOFT CAPS Award (ASE 2018) Dr. Fan has served in various academic service roles including as a program committee member for major conferences such as ASE, ICSE, FSE, and ISSRE. She has also been a reviewer for prestigious journals including IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Software Engineering (TSE), and ACM Transactions on Software Engineering and Methodology (TOSEM).
Brittany Johnson-Matthews is an Assistant Professor in the Department of Computer Science at George Mason University, where she directs the INSPIRED Lab (INterdisciplinary Software Practice Improvement REsearch and Development). Her work bridges software engineering, human-computer interaction, and machine learning to address sociotechnical challenges in software development. Her educational background includes: Ph.D. in Computer Science from North Carolina State University (2017) B.A. in Computer Science from the College of Charleston (2011) Dr. Johnson-Matthews' research centers on sociotechnical problems in software development, with emphasis on developer productivity, tool support, work environments, ethics, and software for social good. She employs interdisciplinary approaches to study how developers interact with tools and environments, particularly in the context of emerging technologies like AI. Her work often involves empirical studies and tool development to promote fairness, inclusivity, and well-being in software engineering. Analysis of her recent publications (2023-2026) reveals a consistent focus on the human aspects of software engineering. Key themes include the impact of AI-assisted tools on developer well-being, fairness in machine learning toolkits, and ethical considerations in software development. Her research frequently involves building and evaluating tools (e.g., for detecting harmful terminology or causal testing) and conducting empirical studies across open source and industrial settings. She leads the INSPIRED Lab, which fosters interdisciplinary collaboration to improve software practices through research in human-centered computing, empirical software engineering, and ethical AI.
Mark Harman is a part-time Professor of Software Engineering at University College London's Department of Computer Science within the Faculty of Engineering Sciences, while working full-time as a Research Scientist at Meta Platforms in the Instagram Product Performance team. He previously served as head of Software Engineering at UCL and director of its CREST centre from 2006 to 2017 before joining Meta when his startup Majicke was acquired in 2017. Harman's research spans multiple domains of software engineering, with particular emphasis on Search Based Software Engineering (SBSE), which he co-founded in 2001. His work has evolved to include LLM-based software engineering, software testing, program analysis, and bias mitigation in machine learning systems. He has made significant contributions to automated testing through systems like Sapienz and WW that have been deployed at scale at Meta. His publication record shows a clear evolution from traditional software testing and analysis toward increasingly sophisticated integration of machine learning techniques. Recent work demonstrates strong focus on addressing fairness challenges in ML systems, improving test reliability in continuous integration environments, and exploring the applications of large language models in software engineering tasks. This reflects both his ability to identify emerging challenges and his commitment to practical, industry-relevant research. IEEE Harlan Mills Award (2019) ACM Outstanding Research Award (2019) Fellowship of the Royal Academy of Engineering (2020) Harman maintains a unique bridge between academia and industry, having co-founded the Simulation-Based Testing team at Meta and previously directing UCL's CREST research centre. His work on Sapienz grew from his startup Majicke and has had significant industrial impact while maintaining strong academic foundations. He frequently participates in academic conferences as both contributor and committee member, demonstrating ongoing commitment to the research community despite his industry position. At Meta, Harman works within the Instagram Product Performance team, building on his earlier work with the Simulation-Based Testing team where he co-developed platforms for client- and server-side testing. His research on cyber-cyber digital twins represents an innovative application of simulation techniques to virtual software systems rather than physical ones.
Shin Hong is an Associate Professor at the School of Computer Science, Chungbuk National University (CBNU) in South Korea. He leads the SDEV lab (소프트웨어 개발검증 연구실) and maintains active roles in major software engineering conferences including ASE 2025 (Local Arrangement Co-Chair), ICST 2026 (General Co-Chair), and SSBSE 2025 (General Chair). His research focuses on Software Testing, Automated Debugging, and Program Analyses , with specific expertise in test case generation, debugging automation, and static/dynamic/neural program analysis techniques. Dr. Hong's work bridges theoretical advances with practical applications, as evidenced by his industrial case study with SAP HANA. Analysis of his recent publications reveals strong trends in fuzzing techniques (ZigZagFuzz, BUGOSS), regression bug benchmarking, mutation analysis, and GUI test case repair. His research demonstrates increasing sophistication in combining traditional software engineering approaches with machine learning methods for test optimization and fault diagnosis. Research Focus Areas: Advanced test generation techniques Automated debugging and fault localization Machine learning applications in software testing Empirical evaluation of testing techniques Dr. Hong actively contributes to the academic community through program committee service at top conferences and mentorship of graduate students at CBNU. His leadership roles in major conferences reflect his standing in the software engineering research community.
Sen Chen is a Professor at Nankai University, holding positions in both the College of Cryptology and Cyber Science and the College of Computer Science. He leads the Nankai Software Security Laboratory (NKSSecLab) and is a member of Professor Zheli Liu's research group. Previously, he served as a tenured associate professor and research professor at Tianjin University (2021-2024), and as a research assistant professor at Nanyang Technological University (NTU), Singapore. Dr. Chen's research focuses on software security and software supply chain security, with particular emphasis on vulnerability analysis and malware detection. His work spans multiple domains including mobile security, AI security, open-source security, and intelligent software development and testing. His research has led to significant contributions in automated security vulnerability detection, software composition analysis, and security tool development for various platforms including Android, Java, and blockchain systems. Analysis of Dr. Chen's recent publications (2023-2025) reveals a strong focus on software supply chain security, with particular attention to vulnerability detection and remediation in open-source ecosystems. His work demonstrates expertise in applying advanced machine learning techniques to security problems, especially in the context of Android applications and containerized environments. There's a clear trajectory toward addressing emerging challenges in AI security and large language model supply chains, reflecting his ability to adapt research directions to evolving technological landscapes. ACM SIGSOFT Distinguished Paper Award (FSE 2024) ACM SIGSOFT Distinguished Paper Award (ASE 2023) First Place of the 13th Challenge Cup China College Students' Entrepreneurship Competition ACM SIGSOFT Distinguished Paper Award (ICSE 2023) Prototype Research Tool Award 2nd Place (Freestyle) in CCF ChinaSoft 2022 First Place of The 8th China International College Students' 'Internet+' Innovation and Entrepreneurship Competition ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM China Rising Star Award (ACM Tianjin Council) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) First Class of Progress of Science and Technology Prize of Tianjin, 2020 Dr. Chen has successfully secured funding from multiple prestigious sources including key R&D programs, general and pre-research projects of the National Natural Science Foundation of China, and the Populus euphratica Forest Fund. His theoretical research has been applied by major companies such as State Grid, China Automotive Industry Corporation, and Huawei. He has mentored students to win national gold medals in both the 'Internet Plus' and Challenge Cup programs, demonstrating his commitment to student development and practical application of research. Dr. Chen leads NKSSecLab (Nankai Software Security Laboratory), which focuses on cutting-edge research in software security and supply chain security. The lab has developed several notable tools including SCTruster (a digital trust chain platform for software supply chain security) and LiDetector. The lab maintains strong international collaborations with institutions like Nanyang Technological University in Singapore and has established itself as a leading research group in software security within China.
