Sofia Bobadilla Ponce is a Doctoral Student at the KTH Royal Institute of Technology , working in the Division of Theoretical Computer Science. Her research focuses on Automated Program Repair for Smart Contracts and improving Code Quality through innovative tools. Civil Engineer in Computer Science (2021–2024) and Bachelor in Computer Science (2017–2021) at University of Chile Research Engineer at KTH (2022–2023) Exchange student at KTH (Spring 2022) Her research spans Smart Contracts , Automated Program Repair , and Software Supply Chain analysis. Recent publications examine exploit mitigation in blockchain systems, LLM-driven program repair, and code smell detection in supply chains. She contributes to education as a course assistant in Algorithms and Data Structures , Automated Software Testing and DevOps , and related fields. Contact: sofbob@kth.se
Alexandra Mendes is an Assistant Professor at the Department of Informatics Engineering, Faculty of Engineering, University of Porto, where she co-leads the Software Reliability Lab. She is also a senior researcher at INESC TEC and a Fellow of The Higher Education Academy (HEA). In 2023, she was a Visiting Researcher at Carnegie Mellon University under the CMU Portugal Program, hosted at CyLab Security and Privacy Institute. Her educational background includes a PhD in Computer Science from the University of Nottingham, UK, and a BSc in Mathematics and Computer Science from Minho University, Portugal. Alexandra's research focuses on encouraging wider adoption of software verification by creating tools and methods that hide the complexities of verifying software. Her primary research interests include: Software Reliability Software Verification Formal Methods Software Engineering Innovative User Interfaces for formal methods Password Security Her recent publications demonstrate a strong focus on applying formal methods to practical software engineering challenges, particularly through the use of Large Language Models to enhance verification-aware languages like Dafny. She has made significant contributions to understanding contract usage in Android applications, infrastructure as code reliability, and password security through formal verification. Her work bridges theoretical formal methods with practical software engineering concerns, aiming to make verification more accessible to developers. Alexandra has received several prestigious awards for her research: Amazon Research Award in Automated Reasoning (Fall 2024) Atlantic Security Award 2024 from the Luso-American Development Foundation (FLAD) for exploring Large Language Models trained on Dark Web data to support decision making for Atlantic security and defence Fellow of The Higher Education Academy (HEA) She actively mentors students and researchers, inviting talented individuals to join her in research opportunities in Computer Science at the University of Porto. Her funded projects include VeriFixer (focused on automated repair techniques for verification-aware programming languages) and InfraGov (addressing challenges in the reliability and security of Infrastructure as Code used in Public Administration). Alexandra co-leads the Software Reliability Lab, which develops new methods and techniques for improving the quality and dependability of software systems, emphasizing practical tools that can have societal impact. The lab's work spans from empirical software engineering methods that can inform practitioners and direct future research, to formal methods that can verify the absence of certain types of bugs.
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.
Yanhui Li is an Assistant Professor at the Software Institute of Nanjing University, specializing in AI software testing and empirical software engineering. Holding a PhD from Southeast University, he actively contributes to both research and teaching in software engineering for AI systems. Institution: Nanjing University, Software Institute Academic Rank: Assistant Professor Teaching: Discrete Mathematics (2023-2025), Data Structure and Financial Algorithm (2016-2024), Advanced Algorithm (2024-2025) His research focuses on AI Testing and Debugging , Mutation Testing , and Empirical Software Engineering with applications in deep learning systems. Key areas include developing testing methodologies for machine learning fairness, word sense disambiguation models, and natural language inference systems. His work bridges theoretical formal methods with practical software analysis techniques to improve AI system reliability. Analysis of recent publications (2023-2025) reveals strong emphasis on testing deep learning components (40% of works), mutation testing adaptations (25%), and empirical studies of software engineering practices (20%). His research increasingly integrates causal analysis with traditional testing techniques, particularly for fairness evaluation in ML systems. 2019 Nanjing University 'Most Loved Teacher' Award (top 9 university-wide) 2020 Nanjing University 'Most Loved Teacher' Award (top 7 university-wide) 2022 & 2024 'Best Course' recognition for Data Structure and Financial Algorithm Dr. Li actively advises students and leads multiple research projects including National Natural Science Foundation funding for 'Semantic based testing data efficacy measurement for deep learning models'. His group recruits PhD and master's students specializing in AI software engineering, with emphasis on testing/debugging AI systems and empirical studies of AI development practices. Current projects include model-based code generation with Nanjing University of Aeronautics and Astronautics and Huawei-funded research on mixed-language programming environments.
Rrezarta Krasniqi is an Assistant Professor of Software Engineering in the Department of Software and Information Systems at the University of North Carolina at Charlotte. Her work focuses on improving software quality through innovative approaches to bug detection and system-wide quality issue analysis. Her educational background includes: B.S. in Mathematics and Computer Science from the University of Prishtina M.S. in Computer Science from Midwestern State University M.S. in Computer Science and Engineering from the University of Notre Dame Ph.D. in Computer Science and Engineering from the University of North Texas Dr. Krasniqi's research centers on quality-related bug detection problems, with particular emphasis on security, usability, and reliability issues that emerge from long-term software maintenance. She develops tools and techniques to enhance understanding of complex system-wide quality issues using code analysis, program comprehension, and AI-driven approaches. Her work combines technical depth with empirical research methodologies to identify root causes of software quality problems and develop more reliable software systems. Her recent publications demonstrate a consistent research trajectory focused on software quality concerns, with increasing integration of NLP and machine learning techniques. Her work spans from foundational bug detection methods to more recent applications of large language models in code translation benchmarking. Dr. Krasniqi actively contributes to the software engineering research community through service on numerous program committees including ASE, ICSE, ICSME, and EASE conferences. In 2025, she served as Tool and Demo Track co-chair for SANER and presented her work on code translation with LLMs at ASE. Before joining UNC Charlotte, she taught computer science courses at various institutions and worked for over three years as a senior Java developer in industry, contributing to web-based application development and maintenance.
Dr. Chetan Arora is a Senior Lecturer in Software Engineering and Director of Education for Software Systems and Cybersecurity at Monash University's Faculty of Information Technology in Australia. With a PhD in Computer Science from the University of Luxembourg and a Master's degree from Technische Universität Kaiserslautern in Germany, he brings both academic rigor and extensive industry experience to his research and teaching. Current Position: Senior Lecturer in Software Engineering at Monash University Leadership Role: Director of Education for Software Systems and Cybersecurity Previous Positions: Academic Director at Deakin University, Innovation Programs at SES Satellites Education: PhD (University of Luxembourg), MSc (TU Kaiserslautern), B.Tech (Thapar University) Dr. Arora's research focuses on the intersection of Software Engineering, Requirements Engineering, and Applied Artificial Intelligence. His work investigates how Natural Language Processing and Machine Learning can improve software reliability and trustworthiness. He has published extensively in top-tier venues including ICSE, FSE, RE, and ASE, with recent publications examining the application of large language models in software engineering practices. His research also extends to satellite communications (particularly dynamic resource allocation) and Internet of Things applications. His work demonstrates clear trends toward integrating AI technologies with traditional software engineering practices, particularly in requirements analysis, quality assurance, and testing. He has secured significant research funding from Australian Defence organizations for projects related to satellite communications and AI-based decision making. His recent publications show growing emphasis on ethical considerations of AI in software development and human-centered aspects of software engineering. 2022 Deakin School of Information Technology International and Partnership Award for Excellence in Advancing International Teaching Partnership 2022 Best Paper Award for Automated Question Answering for Improved Understanding of Compliance Multiple publications in top-tier software engineering conferences and journals As an educator and academic leader, Dr. Arora supervises numerous PhD students working on cutting-edge topics at the AI-software engineering intersection. His industry experience across multiple countries (Australia, Luxembourg, Germany, and India) provides valuable practical perspective to his academic work, particularly in satellite communications, IoT, and AI applications in critical systems. He serves on program committees for major conferences including ICSE, FSE, and RE, contributing to the broader software engineering research community.
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.
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.
Mohamed Wiem Mkaouer is an Associate Professor of Software Engineering in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the principal investigator of the SMILE (Software Maintenance and Intelligent Evolution) Research Lab, which focuses on designing intelligent algorithms to tackle software engineering problems. Dr. Mkaouer has published over 130 peer-reviewed papers in top venues including TSE, TOSEM, EMSE, ICSE, FSE, CHI, and ASE, and has secured $1.5 million in externally funded projects as PI/Co-PI. PhD in Computer and Information Science, 2016, University of Michigan MS in Computer Science, 2010, University of Geneva (Switzerland) BS in Computer Science, 2007, University of Tunis (Tunisia) Dr. Mkaouer's research spans the intersection of Software Engineering and Artificial Intelligence, with particular focus on software quality assurance, systems refactoring, and the application of search-based techniques. His work increasingly explores the integration of large language models in software development processes, examining how AI tools like ChatGPT impact developer workflows, code quality, and refactoring practices. His research often employs empirical methods, mining software repositories, and conducting large-scale studies of developer behavior. Analysis of Dr. Mkaouer's recent publications reveals a strong trend toward investigating AI-assisted software development, particularly focusing on how developers interact with large language models for code refactoring and quality improvement. His work bridges traditional software engineering concerns with emerging AI capabilities, examining both infrastructure-as-code quality and mobile application development challenges. His research methodology typically combines empirical studies of developer practices with the development of intelligent tools to support software maintenance. Recipient of 5 best-paper/presentation awards RIT GCCIS Best Emerging Researcher Award (2020) Dr. Mkaouer actively mentors a diverse group of students at multiple levels, from undergraduate to PhD candidates, through the SMILE Research Lab. His externally funded projects demonstrate the practical relevance of his research to industry needs. He also contributes significantly to the software engineering community through service on program committees for major conferences including ASE, ICSE, and ICSME, where he frequently serves in leadership roles for tracks related to software maintenance, refactoring, and AI in software engineering. The SMILE Research Lab, under Dr. Mkaouer's leadership, maintains strong industry connections and collaborates with researchers worldwide. The lab's work has practical implications for improving software quality, reducing technical debt, and enhancing developer productivity through intelligent tooling and evidence-based practices.
Paris C. Avgeriou is a Professor at the University of Groningen, Netherlands, with a distinguished research career focused on software architecture, technical debt management, and architectural decision making. His work bridges theoretical foundations with practical applications in software engineering, particularly in the areas of software quality, self-adaptive systems, and cyber-physical systems. With over 275 publications and nearly 6,000 citations, his research has significantly influenced the software architecture community. His research interests span multiple critical areas in software engineering: Software Architecture and Architectural Knowledge Management Technical Debt Management across the software lifecycle Architectural Decision Making processes and viewpoints Quality Attributes in Software Systems Self-Adaptive and Cyber-Physical Systems Software Metrics and Measurement Avgeriou's recent work demonstrates a strong focus on practical applications of software architecture principles, with particular attention to technical debt management automation, architectural decision viewpoints, and the intersection of software architecture with emerging technologies like AI. His publications reveal a consistent pattern of empirical research, systematic literature reviews, and case studies that provide both theoretical contributions and practical guidance for industry practitioners. The research shows increasing emphasis on human-centered approaches to software engineering challenges, particularly in understanding practitioner perspectives on technical debt and architectural decisions. Among his notable contributions are systematic studies on technical debt management automation, architectural assumption management, and decision-making processes in software architecture. His work on technical debt has been particularly influential, examining various dimensions including lifecycle analysis, self-fixed debt patterns, and the relationship between technical debt principal and interest. Avgeriou has been actively involved in the software engineering research community, contributing to special issues and editorials that shape research directions in software architecture and technical debt. His recent editorial work demonstrates his commitment to bridging the gap between research and practice in software engineering.
Volker Stolz is an Associate Professor in the Department of Informatics at the University of Oslo, Faculty of Mathematics and Natural Sciences. He is affiliated with the Reliable Systems research group, where his work centers on improving software reliability through formal methods, model transformation, and UML-based modeling. Institution: University of Oslo School: Faculty of Mathematics and Natural Sciences Department: Department of Informatics Research Group: Reliable Systems Contact: stolz@ifi.uio.no | +47 22852438 | Room GA06 9461 His research spans formal verification, concurrency, model-based testing, and programming language semantics. He has made significant contributions to deadlock detection, refactoring equivalence, runtime verification in distributed systems, and data race analysis, often using formal models such as Petri nets and active object languages. The recent publications highlight a consistent focus on software correctness , modular analysis , and automated verification techniques. Trends include the use of behavioral effects, abstract execution, and field calculus for distributed monitoring. His work frequently appears in top-tier venues like Theoretical Computer Science , Lecture Notes in Computer Science , and Journal of Logical and Algebraic Methods in Programming , indicating strong theoretical and practical impact. He collaborates extensively with researchers such as Violet Ka I Pun, Rui Wang, Lars Michael Kristensen, and Martin Steffen, reflecting an active and collaborative research profile. No scientific awards are mentioned in the provided text. There is no information available about student advising or research grants. Volker Stolz is involved in research projects related to model-based testing, formal methods, and reliable software systems, particularly through the Reliable Systems group. His work often involves building theoretical foundations and practical tools for verifying and improving software behavior in distributed and concurrent environments.
Alok Mishra is a Professor at Molde University College, affiliated with the Faculty of Logistics. His research focuses on Artificial Intelligence, Software Engineering, Cybersecurity, and Digitalization, with a strong emphasis on blockchain technology, machine learning, and sustainability. He leads the research group 'Digitalization for Sustainability Informatics and Digitalization.' Key projects include 'Building Trust in Global Seafood Supply Chains Through DNA Analysis, Digital Product Passports (DPPs), and Blockchain Technology.' His work spans interdisciplinary areas such as AI ethics, data privacy, and IoT-based systems. Recent publications highlight advancements in code smell detection, cybersecurity policy development, and green AI initiatives. His research trends reflect a blend of theoretical and applied studies, addressing challenges in software quality, blockchain integration, and sustainable tech solutions. Collaborations with global researchers underscore his commitment to impactful, cross-disciplinary innovation.
Dr. Masud Rahman is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads the RAISE Lab. He earned his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan under Prof. Chanchal Roy and completed a postdoctoral fellowship at Polytechnique Montreal with Prof. Foutse Khomh. Ph.D., Computer Science/Software Engineering, University of Saskatchewan Postdoctoral Fellow, Polytechnique Montreal M.Sc., Computer Science, University of Saskatchewan B.Sc., Computer Science, Khulna University His research focuses on the intelligent automation of software maintenance and evolution, particularly in debugging, bug localization, code search, and mining software repositories. He integrates Artificial Intelligence with Software Engineering to tackle challenges in modern software systems, including those involving Large Language Models, Deep Learning, and Cloud Computing. His work addresses critical industry problems such as software bugs, crashes, vulnerabilities, and technical debt, aiming to reduce the immense economic cost of software failures. Dr. Rahman’s recent publications show a strong trend in applying AI and machine learning techniques to software engineering problems, especially in deep learning systems, reproducibility of bugs, and automated code analysis. His work frequently appears in top-tier venues like ICSE, FSE, ASE, TOSEM, and EMSE. Scientific Awards: Governor General's Gold Medal U of S Doctoral Thesis Award Best PhD Thesis Award (Computer Science) Dr Keith Geddes Award Dalhousie Belong Research Fellowship President Gold Medal (Bangladesh) TCSE Distinguished Paper Award (2020) Best Reviewer Award (2 times) Best Paper Award (2 times) Dr. Rahman has successfully advised multiple graduate students and secured over $475K as Principal Investigator and $4.3M as Co-PI from competitive grants including NSERC Discovery, Mitacs Accelerate International, NSERC Alliance, and Dalhousie Startup Fund. He actively contributes to the academic community as a Guest Editor for EMSE Special Issue (SANER 2025), PC Chair, PC Member, and journal reviewer. He leads the RAISE Lab, which focuses on R esearch in A rtificial I ntelligence and S oftware E ngineering, fostering innovation in sustainable software and AI systems.
Boris Cherry is a Research Fellow at the Faculty of Computer Science, University of Liège, specializing in software engineering with focus on static program analysis and database systems. His educational background includes: PhD in Sciences (2024) with thesis on "Empirical Investigation and Static Detection of Code Smells in Applications using Document-Oriented Datastores" Master in Computer Science (2020) with thesis on "JCrashPack2.0: Search-based crash reproduction hardness analysis" Cherry's research centers on static analysis techniques for database applications, particularly MongoDB. He investigates code smells, crash reproduction mechanisms, and software quality metrics through empirical studies and tool development. His work bridges theoretical program analysis with practical database application challenges, emphasizing empirical validation and reproducibility in software engineering research. Publication trends (2020-2024) reveal consistent specialization in MongoDB-related static analysis, evolving from crash reproduction studies to comprehensive code smell detection frameworks. His research combines database theory, software quality metrics, and empirical validation methods, with strong emphasis on tool development for practical industry application. Cherry actively contributes to academic community building through event organization, including co-organizing the 2024 PhD Student Day for the Faculty of Computer Science. His collaborative work with the Software Evolution and Analysis Group demonstrates strong institutional integration and interdisciplinary research approaches.
Daniel Varro is a Professor and Head of Unit at the Department of Computer Science (IDA) of Linköping University, Sweden. He leads the Software and Systems (SAS) department, focusing on AI, software engineering, and cyber-physical systems. His research is supported by major grants like the Vinnova 5.6 million SEK project for AI-generated software quality assurance. Affiliation: Department of Computer Science (IDA), Linköping University Department: Software and Systems (SAS) Research Focus: Model-based systems, large language models for code analysis, reinforcement learning, and cyber-physical safety verification. His recent work includes empirical studies on machine learning notebooks, infrastructure code smells, and data leakage in large language models. He collaborates extensively within the Wallenberg Autonomous Systems Program (WASP) and trains doctoral students in software engineering.
