Lina Gong is an Associate Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. She holds a Ph.D. in Computer Software and Theory from China University of Mining and Technology (2020) and completed a research visit at Queen's University's Software Analysis and Intelligence Lab (SAIL) under Prof. Ahmed Hassan (2019-2020). Her research focuses on leveraging machine learning to extract insights from software repositories, with emphasis on: ML-enabled defect prediction techniques Code pre-trained models for vulnerability detection Identifier normalization and issue classification Empirical studies of software quality attributes Her recent publications (2023-2025) demonstrate strong trends in applying transformer architectures to code analysis, with increasing focus on supply chain security and cross-platform UI translation. Key venues include IEEE TSE, ACM TOSEM, and ASE. Scientific recognition includes: National Natural Science Foundation of China (2022-2025) Natural Science Foundation of Jiangsu Province (2022-2025) Key National Laboratory Foundation (2022-2023) Excellent Ph.D. Student Award (CUMT) She actively mentors graduate students (14 advisees: 1 doctoral, 13 master's) and serves on program committees for ASE, APSEC, and SANER. Her research is supported by multiple competitive grants focusing on ML applications in software engineering.
Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.
Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.
Dr. Preetha Chatterjee is an Assistant Professor in the Department of Computer Science at Drexel University's College of Engineering, where she leads the SOftware Engineering and Analytics Research (SOAR) Lab. Her academic career spans software engineering research, teaching, and service, with a focus on improving developer productivity through advanced analytics and tools. Dr. Chatterjee earned her M.S. and Ph.D. in Computer Science from the University of Delaware, advised by Dr. Lori Pollock, following 5+ years of industry experience as a Software Engineer. Her educational background bridges practical industry experience with rigorous academic training. Her research focuses on Software Engineering with emphasis on developing tools, knowledge sources, and strategies to support software maintenance and improve developer productivity. She incorporates evidence from mining software repositories, conducting empirical studies, and adapting state-of-the-art techniques from Natural Language Processing and Machine Learning. Her current research directions include LLM-assisted software development and maintenance, developer collaboration in distributed software teams, and knowledge extraction from large-scale software artifacts. She has made significant contributions to emotion mining in developer communications, toxicity detection in open source projects, and trust dynamics in GitHub pull requests. Dr. Chatterjee's publications demonstrate a clear progression from foundational work on mining developer chat communications and code snippets toward more sophisticated applications of machine learning and large language models in software engineering contexts. Her recent work increasingly focuses on the intersection of software engineering with social aspects like emotions, trust, and toxicity in developer interactions. Distinguished Reviewer Award at FSE 2023 Drexel CCI Research Excellence Award (awarded to her lab member Ramtin Ehsani) Drexel CS Leadership Award (awarded to her lab member Amirali Sajadi) Dr. Chatterjee has advised numerous students at various levels, including Ph.D., M.S., and undergraduate researchers. Her SOAR Lab currently includes Ph.D. students Ramtin Ehsani and Amirali Sajadi, who have received significant recognition for their work. She has served on multiple program committees for major software engineering conferences including ICSE, FSE, ASE, and MSR, and has held leadership roles such as Tutorials Co-chair for MSR 2025 and Journal-first Co-Chair for ICPC 2024. She co-leads the Drexel Programming Systems Seminar and has been an editorial board member for the Journal of Systems and Software. The SOAR Lab focuses on innovative research at the intersection of software engineering, machine learning, and natural language processing. The lab has produced influential datasets like DISCO (Discord Chat Conversations for Software Engineering Research) and comprehensive annotated datasets of GitHub issue threads. Current projects include improving LLM-assisted bug resolution, security assessment of LLM-generated code, emotion mining from software engineering communication, and information extraction from developer chat conversations.
Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.
Shangwen Wang is an Assistant Professor in the School of Computer Science at National University of Defense Technology (NUDT) in Changsha, China. He earned his Bachelor's degree in June 2017, Master's degree in December 2019, and Ph.D. in December 2023, all from NUDT. During his graduate studies, he was supervised by Professor Xiaoguang Mao. From May 2022 to July 2023, he was a visiting student at Southern University of Science and Technology under Professor Yepang Liu. His educational background includes: Ph.D. in Software Engineering, NUDT (2020.3-2023.12), supervised by Prof. Xiaoguang Mao Visiting Scholar, SUSTech (2022.5-2023.7), supervised by Prof. Yepang Liu M.A. in Software Engineering, NUDT (2017.9-2019.12), supervised by Prof. Xiaoguang Mao B.A. in Software Engineering, NUDT (2013.9-2017.6) Wang's research focuses on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for Software Engineering. His work bridges traditional software engineering techniques with modern AI approaches, particularly leveraging large language models for various software engineering tasks. He has made significant contributions to automated program repair, fault localization, vulnerability detection, and code generation. His research demonstrates a strong emphasis on empirical validation and practical applicability to real-world software development challenges. His recent publications show a clear trend toward integrating large language models with traditional software engineering tasks. The 15 most recent articles reveal a focus on applying LLMs to program repair, fault localization, vulnerability detection, and code generation, while maintaining strong empirical foundations. His work spans both theoretical advancements and practical tool development, with applications in software security, testing, and maintenance. His notable achievements include: CCF Outstanding Doctoral Dissertation (CCF优博) 2024 Outstanding Doctoral Graduates, NUDT, 2023 Multiple distinguished paper awards including ACM SIGSOFT Distinguished Paper Award (ISSTA'24) and IEEE TCSE Distinguished Paper Awards (ICSME'22, SANER'22) Prestigious scholarships from NUDT throughout his academic career As an active member of the software engineering community, Wang serves on numerous program committees for top conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He has also contributed to teaching as a teaching assistant for courses such as Compiler, Python Programming, Discrete Mathematics, and C++ Programming. His research group appears to be actively mentoring students, as evidenced by his role as corresponding author on multiple student-led publications. Wang maintains an active research presence with collaborations across multiple institutions in China. His work demonstrates a clear trajectory from traditional program analysis techniques toward integrating cutting-edge AI approaches, particularly large language models, into software engineering practices.
Professor Sven Apel holds the Chair of Software Engineering at Saarland University's Saarland Informatics Campus in Germany. He is also the Director of the Saarbrücken Graduate School of Computer Science. His work focuses on software engineering with an emphasis on automation, human factors, and interdisciplinary approaches. Prof. Apel received his Ph.D. in Computer Science in 2007 from the University of Magdeburg. His academic journey includes: Ph.D. in Computer Science, University of Magdeburg (2007) Emmy-Noether Fellowship of the German Research Foundation Heisenberg Professorship of the German Research Foundation Prof. Apel's research centers on empowering software engineering practice to enter an era of intensive automation. His key research areas include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and empirical and neurophysiological methods. He pays special attention to the human factor and interdisciplinary research questions, applying his findings to real-world software systems from both open-source projects and industry collaborations with partners like Siemens AG, Bosch Engineering, and Airbus Helicopters. Analysis of Prof. Apel's recent publications reveals a strong focus on configurable software systems, neurophysiological approaches to understanding programming, and the application of AI techniques to software engineering problems. His work often bridges the gap between theoretical foundations and practical applications, with many studies involving industrial collaborations. There's a noticeable trend toward interdisciplinary research combining software engineering with neuroscience, organizational studies, and machine learning. Prof. Apel has received numerous prestigious awards and honors: ERC Advanced Grant "Brains On Code" (2022) ACM Distinguished Member for "Outstanding Scientific Contributions to Computing" (2018) Multiple Most Influential Paper Awards (SPLC'18, ICPC'22, GPCE'23) Multiple Best Paper Awards (SPLC'11, Modularity'15, AOM'18) Heisenberg Professorship and Emmy-Noether Fellowship from the German Research Foundation Prof. Apel has advised numerous Ph.D., Master's, and Bachelor's students throughout his career. His research has been generously funded by multiple grants including an ERC Advanced Grant (2,500,000 Euro, 2022-2027), several DFG projects (CPEC, Congruence, Pervolution), and previous grants like SafeSPL, FeatureFoundation, and Pythia. His work has practical impact through collaborations with industry partners including Siemens AG, Bosch Engineering, and Airbus Helicopters. Prof. Apel leads research in the Chair of Software Engineering at Saarland University, where his team explores the intersection of software engineering, neuroscience, and artificial intelligence. His "Brains On Code" ERC project specifically investigates how programmers' brains process code using neuroimaging techniques. The research group maintains strong connections with both academic and industry partners, facilitating the transfer of research findings into practical applications.
Andreas Zeller serves as Professor for Software Engineering at Saarland University and faculty at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. His dual appointments position him at the intersection of academic research and practical cybersecurity applications, contributing significantly to both institutions' research profiles. Professor Zeller's research spans multiple dimensions of software quality assurance, with particular expertise in automated debugging techniques, mining software repositories for insights, specification mining, and security testing methodologies. His work consistently bridges theoretical foundations with practical implementation, resulting in tools and frameworks adopted widely in both research and industry contexts. Analysis of his recent publications reveals an evolutionary trajectory from foundational debugging work toward increasingly sophisticated grammar-based testing approaches, with notable integration of machine learning techniques in recent years. His research demonstrates consistent focus on improving software reliability through automated analysis, with growing emphasis on security applications including XML injection testing, GNSS module security, and binary file format vulnerabilities. Recipient of two ERC Advanced Grants (including the S3 project) ACM Fellow ACM SIGSOFT Outstanding Research Award Professor Zeller has successfully secured substantial research funding through competitive mechanisms including ERC grants, enabling his team to pursue ambitious research agendas. His leadership extends to mentoring through his roles as doctoral symposium co-chair and active participation in new faculty development initiatives. He maintains strong engagement with the research community through numerous program committee memberships and conference organization roles. At CISPA Helmholtz Center for Information Security, Zeller contributes to the center's mission through research focused on software security testing and analysis. His work on grammar-based testing and fuzzing directly addresses critical security challenges in modern software systems, with practical applications for improving software resilience against attacks.
Shane McIntosh is an Associate Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he leads the Software Repository Excavation and Build Engineering Labs (Software REBELs). His academic career focuses on empirical studies of software development processes with particular emphasis on release engineering and software quality. Dr. McIntosh's research centers on mining historical data generated during software development to derive practical insights for building more reliable systems. His work spans release engineering (assembling, verifying, and delivering software releases) and software quality (developing guidelines for reliable software). This research manifests in studies of continuous integration systems, build outcome prediction, defect prediction models, and code review practices. His publication record reveals a consistent focus on empirical software engineering with recent papers examining build system reliability, continuous integration practices, and defect prediction. The research demonstrates strong methodological rigor through replication studies, longitudinal analyses, and large-scale data mining of software repositories. His work bridges theoretical insights with practical applications for software development teams. Dr. McIntosh actively contributes to the software engineering community through substantial service roles including Proceedings Co-chair for ICSE 2022, General Chair for PROMISE 2021-2022, and committee positions across major conferences like ASE, ESEC/FSE, and MSR. His teaching portfolio includes foundational courses such as Introduction to Software Engineering, Software Analytics, and Software Delivery. He directs the Software REBELs lab, which provides a collaborative environment for investigating software development data. The lab's work focuses on extracting meaningful patterns from version control systems, issue trackers, and continuous integration pipelines to improve software engineering practices.
Lu Xiao is an Assistant Professor in the School of Systems and Enterprises at Stevens Institute of Technology, where she conducts research in software engineering with a focus on software architecture, software economics, cost estimation, and software ecosystems. Dr. Xiao completed her PhD in Computer Science at Drexel University in 2016 under the supervision of Dr. Yuanfang Cai. Her doctoral research focused on the relationship between software architecture and quality attributes. Her research spans several key areas in software engineering with emphasis on empirical methods. She investigates how software architecture influences quality attributes, studies software economics and cost estimation techniques, and examines the dynamics of software ecosystems. Her work frequently analyzes real-world software projects, particularly those in the Apache Software Foundation, providing insights into software maintenance patterns, testing practices, and performance issues. Dr. Xiao has developed practical tools like SAIN for software architecture infrastructure and eFish'nSea for performance education. Analysis of Dr. Xiao's publication record reveals consistent contributions to empirical software engineering, particularly in software architecture analysis, testing methodologies, and performance issues. Her research often centers on Apache projects, demonstrating methodological rigor in studying real-world development practices. She has made significant advances in understanding test refactoring, mocking frameworks, and the identification of performance bottlenecks through linguistic analysis of issue reports. Dr. Xiao has actively contributed to the software engineering research community through service on program committees for major conferences including ASE, ICSE, ESEC/FSE, and ICSA. Her work on program committees spans multiple tracks including Research Papers, Student Research Competition, and specialized workshops. As an academic advisor, Dr. Xiao guides graduate students in research on software architecture analysis, testing practices, and performance optimization. Her research methodology combines empirical analysis of large software repositories with tool development and validation, ensuring practical relevance of her findings. Dr. Xiao leads research efforts focused on understanding the relationship between software architecture and quality attributes. Her current work on bots in pull requests represents cutting-edge research into automation in open source development processes, continuing her tradition of investigating real-world software engineering phenomena.
Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.
Maxime Lamothe is an assistant professor at Polytechnique Montreal specializing in empirical software engineering and mining software repositories. His research focuses on software APIs, build systems, and the intersection of AI and software engineering. Previously, he was a postdoctoral researcher at the University of Waterloo's Software REBELs Lab under Prof. Shane McIntosh. Dr. Lamothe's educational background includes: Ph.D. in Software Engineering from Concordia University (2020) M.Eng from Concordia University (2017) B.Eng from McGill University (2013) His research interests center around empirical studies of software engineering practices, with particular focus on API design and evolution, software build systems, and performance analysis. Dr. Lamothe investigates how developers interact with APIs, how build systems operate in practice, and how AI techniques can enhance software engineering processes while maintaining human oversight of critical decisions. Dr. Lamothe's publication record shows a consistent focus on empirical approaches to understanding software engineering practices. His work frequently examines API usage patterns, code review processes, and continuous integration systems. A notable trend is his growing interest in applying AI techniques to software engineering challenges while maintaining empirical validation of proposed solutions through rigorous case studies and longitudinal analyses. Dr. Lamothe actively serves the academic community as a reviewer for top journals including Transactions on Software Engineering (TSE), Empirical Software Engineering (EMSE), and Journal of Systems and Software (JSS). He has served on program committees for major conferences including ASE, ICSE, ESEC/FSE, MSR, and SANER across multiple years, with particular involvement in the NIER Track, Research Papers track, and Tool Demonstration tracks. Currently seeking Masters and Ph.D. students, Dr. Lamothe leads research at the intersection of traditional software engineering practices and emerging AI techniques. His work combines rigorous empirical methods with practical applications to solve real challenges in software development, with implications for improving API design, enhancing code review processes, optimizing build systems, and developing trustworthy AI-assisted software engineering tools.
Thomas Degueule is a researcher at CNRS (Centre National de la Recherche Scientifique) in France, actively contributing to software engineering research since 2015. He serves on program committees for major conferences including ASE, ICSE, and SLE, with primary research interests in Software Evolution, Empirical Software Engineering, and Domain-Specific Languages. His work focuses on breaking change analysis in APIs and libraries, client-library compatibility testing, and dependency management. He develops practical tools like Roseau for source-based breaking change detection and investigates semantic versioning impacts in ecosystems like Maven Central. His empirical approach leverages large-scale repository analysis to address real-world software maintenance challenges, particularly in Java ecosystems. Recent publications (2023-2025) show consistent contributions to breaking change analysis and compatibility testing, appearing in top venues like ASE, ICSE, and ISSTA. His research bridges theoretical insights with practical tooling for software evolution challenges, demonstrating strong empirical methodology and tool-oriented contributions. No scientific awards are documented in the available information. Degueule has advised no publicly listed students and holds no mentioned research grants. His organizational roles include Program Co-Chair for SLE 2023 and committee positions across multiple conferences, reflecting significant service to the software engineering community.
Gian Luca Scoccia is an Assistant Professor at the Gran Sasso Science Institute (GSSI) in Italy, where he also completed his PhD in 2019. He maintains active roles in major software engineering conferences as a program committee member for ASE (2024-2025) and SANER (2026). His academic background includes: PhD from Gran Sasso Science Institute, Italy (2019) Scoccia's research centers on empirical software engineering with specialized focus areas including mobile app security , software repository mining , and program analysis . His recent work increasingly integrates artificial intelligence into software development practices, particularly examining ethical implications in autonomous systems and energy efficiency in LLM-driven applications. He employs rigorous empirical methodologies across all research domains. Analysis of his 2023-2025 publications reveals strong convergence between traditional software engineering and AI innovation, with significant emphasis on mobile contexts, developer tooling, and ethical frameworks. His work consistently combines theoretical architecture design with empirical validation through user studies and systematic assessments. Scoccia demonstrates deep community engagement through program committee service for ASE and SANER conferences, contributing to the advancement of software engineering research standards and discourse.
Alexander Serebrenik is a full professor of social software engineering at the Eindhoven University of Technology in the Netherlands, working within the Department of Mathematics and Computer Science. His academic profile spans decades of interdisciplinary research bridging computer science and social sciences. Professor Serebrenik's research focuses on facilitating software evolution through understanding social aspects of development. His work integrates computer science methods (socio-technical coordination theory, natural language processing, machine learning) with organizational psychology principles. A consistent theme across his publications is empiricism - addressing software engineering challenges through observation and experimentation while balancing social and technical perspectives. His recent work increasingly emphasizes diversity, equity, and inclusion in software engineering, culminating in his 2024 book "Equity, Diversity, and Inclusion in Software Engineering: Best Practices and Insights" (APress). His publication record reveals evolving interests from socio-technical coordination to human factors, community dynamics in open source, and now DEI-focused research. Distinguished Paper Award at ICSE 2023 Distinguished Paper Award at MSR 2023 Distinguished Reviewer Award at FSE 2020 Senior member of IEEE Member of ACM As an academic leader, Professor Serebrenik has mentored PhD students including Tukaram Muske, and actively participates in doctoral symposia. His service includes roles as Diversity and Inclusion Co-Chair at multiple conferences and extensive program committee participation across the software engineering conference landscape. His research group at TU/e maintains an active program studying the social dimensions of software engineering through empirical investigations, contributing significantly to understanding how human factors influence development processes and outcomes.
