Find Academic Supervisors

• Corporate Governance
• Taxation
• Business Succession
• +3 more

Prof. Dr. Werner Volz serves as Professor of General Business Administration (specializing in taxation and finance) at University of Applied Sciences Konstanz since 1993 and is a founding member of the Konstanz Institute for Corporate Governance (KICG). He maintains dual professional engagement as Managing Director of Dr. Volz Tax Consulting GmbH while holding memberships in the Chamber of Auditors, Chamber of Tax Consultants, and CDU Economic Council. He earned his doctorate in Business Administration from the University of Bayreuth in 1988, combining academic credentials with professional certifications as tax advisor and certified public accountant. His international experience includes guest lectureships at Swiss German University in Jakarta (since 2003) and Aston Business School (1984-1988). Prof. Volz's research centers on corporate governance in family businesses, investigating critical intersections between tax regulations and business decision-making. His work examines business succession planning, company valuation methodologies, acquisition tax implications, and international tax frameworks - all with practical applications for medium-sized enterprises. This applied focus reflects the mission of University of Applied Sciences institutions to bridge theoretical knowledge and industry practice. His publication record demonstrates consistent expertise in practical tax issues, highlighted by the 2022 expert opinion on Büsingen tax allowances and 2009 analysis of inheritance tax reform. These works reveal enduring attention to how tax policy affects real business operations, particularly within family-owned contexts where succession and valuation complexities arise. As an active practitioner, Prof. Volz provides tax advisory services to financial intermediaries and serves as expert witness in company valuation cases. His professional engagements directly inform his academic work, creating a feedback loop between classroom instruction and contemporary business challenges. The KICG affiliation positions him within a dedicated corporate governance research ecosystem focused on German-medium enterprise contexts.

Kelvin AnggaraView profile

Assistant Professor

• Glycoscience
• Single Molecule Chemistry
• Nanoscience
• +4 more

Kelvin Anggara serves as an ERC Group Leader at the Max Planck Institute for Solid State Research in Stuttgart, Germany, leading independent research in Nanoscience with faculty-equivalent responsibilities equivalent to Assistant Professor rank. His academic background includes: PhD in Chemistry from University of Toronto (2013-2018) under Prof. John C. Polanyi Dr. Anggara's research pioneers single-molecule analysis of glycans and glycoconjugates using electrospray ion beam deposition combined with scanning probe microscopy (ESIBD+SPM). His ERC-funded GlycoX project deciphers the 'sugar code' by imaging molecular structures one-at-a-time, overcoming limitations of ensemble-averaged techniques. This work bridges glycoscience, nanotechnology, and analytical chemistry with direct implications for cancer diagnostics and vaccine development. His scientific recognition includes: ERC Starting Grant for GlycoX project Alexander von Humboldt Postdoctoral Fellowship As principal investigator, he secures major research funding and mentors early-career scientists through hands-on training in advanced microscopy and computational modeling. His group maintains active collaborations with leading institutions across Europe including University of Oxford and Max Planck Institute of Colloids and Interfaces. The laboratory operates state-of-the-art facilities including low-temperature STM (11 K) and computational resources for DFT modeling (ORCA, VASP, OpenMX), focusing on electronic structures, primary sequencing, and secondary folding of biomolecules.

Nicolai SiegelView profile

Professor

• Molecular Parasitology
• Pathogen Biology
• 3D Genome Architecture
• +7 more

Nicolai Siegel leads the Siegel Lab at the Department of Physiological Chemistry within the Biomedical Center Munich at Ludwig Maximilian University of Munich. His research group is affiliated with both the Faculty of Veterinary Medicine and the Faculty of Medicine at LMU Munich, and is associated with the Chair of Experimental Parasitology led by Markus Meissner. The lab focuses on understanding molecular mechanisms of pathogen biology with emphasis on Trypanosoma brucei , the parasite causing African sleeping sickness. Dr. Siegel's research interests center on cell-to-cell heterogeneity in pathogen populations, particularly how differences in genome sequence, chromatin organization, and 3D genome architecture influence antigenic variation. His work explores how non-coding RNA contributes to 3D genome architecture organization and how RNA processing hotspots function as 'post-transcriptional' enhancers. The lab employs diverse methodologies including genome-wide chromosome conformation capture (Hi-C and Micro-C), RADICL-seq, ribosome profiling, ChIP-based methods, and single-cell RNA-seq to investigate these mechanisms. Analysis of recent publications reveals a strong focus on the relationship between 3D genome architecture and antigenic variation in trypanosomes, with increasing incorporation of single-cell technologies and computational approaches. The work demonstrates how chromatin organization, RNA processing, and transcription hubs collectively regulate monogenic antigen expression in these pathogens. ERC Consolidator Grant awarded to Siegel Lab (2022) DFG funding for studying antigenic variation in Uganda and Kenya Part of EU-funded Cell2Cell integrative training network focusing on cell-to-cell heterogeneity Dr. Siegel advises numerous PhD students and postdoctoral researchers, with recent lab members including Zhibek Keneskhanova, Rodrigo Cosentino, and Vanessa Luzak. His research is supported by multiple grants including ERC funding and DFG projects. The lab maintains collaborations across Europe and Africa, with field work conducted in sub-Saharan Africa to study pathogens in their natural context. The Siegel Lab maintains computational resources including genome assemblies for T. brucei and develops bioinformatics tools like Smoother for interactome data processing. The group is part of the international Cell2Cell network that brings together experts in chromatin biology, single-cell analysis, imaging, and bioinformatics from institutions across Europe, North America, and Asia.

Jiachi ChenView profile

Associate Professor

• Software Security
• Program Analysis
• Smart Contracts
• +4 more

Jiachi Chen is an Associate Professor at the School of Software Engineering, Sun Yat-sen University, China. He received his Ph.D. from Monash University, Australia, and has established himself as a leading researcher in blockchain security and Web3 technologies. His educational background includes: Ph.D. in Information Technology from Monash University (2019-2022) Research Assistant at Hong Kong Polytechnic University (2016-2018) Dr. Chen's research spans software security, program analysis, smart contracts, and Web3 technologies. His work bridges traditional software engineering with blockchain, focusing on security vulnerabilities, large language models for code generation, and empirical studies of smart contract development. Recent publications show increasing integration of AI techniques to address blockchain security challenges. His publication trends reveal deep expertise in smart contract security, with numerous papers on vulnerability detection and code analysis. Recent work has expanded into LLM applications for software engineering, particularly examining code generation quality and security implications in Web3 contexts. Dr. Chen has received significant recognition: Multiple ACM SIGSOFT Distinguished Paper Awards (ICSE 2025, ISSTA 2024, Internetware 2024) Best Paper Award at IEEE CSCloud/EdgeCom 2023 Best Paper Award at IEEE INFOCOM 2018 Best Paper Award at ISPEC 2017 He serves on program committees for top software engineering conferences (ICSE, FSE, ASE, ISSTA) and is actively recruiting students in Web3/Blockchain/Smart Contract research areas. His work appears consistently in premier venues including IEEE Transactions on Software Engineering and ACM conferences, demonstrating sustained research impact.

Ajay JhaView profile

Assistant Professor

• Software Engineering
• Software Testing
• Software Maintenance
• +4 more

Ajay Jha is an Assistant Professor in the Department of Computer Science at North Dakota State University (NDSU), where he leads the Software Testing and Maintenance (STAM) Lab. His academic journey began with industry experience, followed by graduate studies and postdoctoral research that shaped his current focus on software engineering research. His educational background includes: Ph.D. in Computer Science from Kyungpook National University (2017) Master's degree from Kyungpook National University (2013) Over five years of industry experience before graduate studies, including co-founding two startups Postdoctoral research at University of Alberta (over two years) and Kyungpook National University (three years) Dr. Jha's research focuses on software engineering, particularly in the areas of software testing, maintenance, and evolution. His work centers on mining large-scale software repositories to uncover real-world issues in software quality, reliability, and maintainability. He develops innovative tools and techniques to address challenges in regression testing, library migration, and mobile application development. His research has significant practical implications for improving software development processes and enhancing the reliability of modern software systems, particularly in mobile and Python environments. His publication record shows a clear progression from Android-focused research to broader software engineering challenges, with recent work emphasizing Python library migration and large language models for software engineering tasks. His research methodology typically involves empirical studies of real-world software repositories combined with tool development to address identified challenges. Dr. Jha is actively involved in academic service, serving on program committees for major software engineering conferences including MSR, ICSME, SANER, ASE, and ICSE. He also reviews for prestigious journals such as IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. At NDSU, he teaches a range of courses from undergraduate to graduate level, including Mobile Software Engineering, Software Development Processes, and Software Project Planning and Estimation. He also leads graduate seminars on specialized topics like 'LLMs for Software Testing and Maintenance' and 'Code Smell and Refactoring.' He leads the Software Testing and Maintenance (STAM) Lab at NDSU, which focuses on mining software repositories to identify quality issues and developing practical tools to address software maintenance challenges. The lab's research has produced several benchmarks (PyMigBench, JTestMigBench) and tools (TRec) that have been shared with the research community.

Xiang ChenView profile

Associate Professor

• Empirical Software Engineering
• Mining Software Repositories
• Software Testing and Maintenance
• +5 more

Xiang Chen is an Associate Professor at the Department of Software Engineering, School of Artificial Intelligence and Computer Science, Nantong University, China. He received his B.Sc. degree from Xi'an Jiaotong University in 2002 and his M.Sc. and Ph.D. degrees in computer software and theory from Nanjing University in 2008 and 2011 respectively. He is an editorial board member of Information and Software Technology and serves as a program committee member for prestigious conferences including FSE 2026 and ASE 2025. Chen is also a senior member of the China Computer Federation (CCF) and active in various academic committees. Chen's research focuses on empirical software engineering, mining software repositories, and software testing and maintenance, with particular emphasis on applying AI techniques to software engineering problems. His work spans large language models for software engineering, security vulnerability analysis, code change representation, and regression testing. He has published over 110 papers in top-tier journals and conferences including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. His recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models, with traditional software engineering practices. The research spans code generation evaluation, deep learning framework testing, vulnerability detection, and automated program repair, showing a consistent focus on improving software quality through innovative testing and analysis techniques. ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICPC 2023) Top 1% CNKI Highly Cited Scholar (2024) Top 2% Scientist by Stanford University (2023-2025) NASAC 2019 Prototype Competition First Prize Chen has successfully advised numerous graduate and undergraduate students who have gone on to prestigious institutions including Nanjing University, Tsinghua University, and Zhejiang University. Many of his students have won national programming competitions and received scholarships. His research group, smartSE, actively works on projects funded by the Natural Science Foundation of China and various provincial research programs. Chen also serves as a reviewer for top journals including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.

Alberto Martin-LopezView profile

Research Fellow

• Software Testing
• Test Automation
• Web Services
• +3 more

Alberto Martin-Lopez is a postdoctoral fellow in the SEART research group at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. His academic journey includes a PhD from the SCORE Unit of Excellence at the University of Seville (Spain), where he also earned a Bachelor's degree in Telecommunications Engineering and a Master's degree in Software Engineering and Technology. He has held positions as a Fulbright fellow at the University of California, Berkeley and as an external lecturer at Kristiania University College in Oslo, Norway. His research focuses on software testing, service-oriented computing, and neuro-symbolic AI applications to software engineering problems. Martin-Lopez has made significant contributions to automated testing of web services, particularly RESTful APIs, developing tools and techniques for test oracle generation, test input generation, and metamorphic testing. His work bridges traditional software engineering methods with modern AI techniques to address longstanding challenges in software verification. His publications in top-tier venues like ESEC/FSE, ISSTA, and TSE demonstrate consistent impact in the software engineering community. Analysis of his recent work shows a clear trajectory toward integrating neuro-symbolic AI approaches with traditional testing methodologies, particularly for solving the oracle problem in API testing and enhancing code generation systems. His scientific achievements have been recognized with prestigious awards: First Prize of the ACM Student Research Competition at ICSE'20 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'22 2023 Early Career Researcher Award by the Spanish Society of Computer Science and Fundación BBVA Martin-Lopez actively contributes to the software engineering community through service on program committees for major conferences including ASE, ESEC/FSE, ICSE, and ISSTA. His collaborative work spans institutions across Spain, Switzerland, the United States, and Norway, reflecting a strong international research network. At USI, he works within the SEART research group, focusing on advancing the state of the art in automated software testing through innovative combinations of traditional software engineering techniques and artificial intelligence.

Fang LiuView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence for Software Engineering
• Program Understanding
• +3 more

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

Michael PradelView profile

Professor

• Software Engineering
• Programming Languages
• Program Analysis
• +6 more

Michael Pradel is a full professor at the University of Stuttgart and a faculty member at the CISPA Helmholtz Center for Information Security. He leads the Software Lab at the University of Stuttgart and holds additional affiliations with the International Max Planck Research School (IMPRS) for Intelligent Systems and the Stuttgart ELLIS Unit. Previously, he served as an assistant professor at TU Darmstadt, a postdoctoral researcher at UC Berkeley, and a lecturer and postdoctoral researcher at ETH Zurich, where he completed his PhD. His educational background includes computer science studies at TU Dresden and engineering studies at Ecole Centrale Paris, with a master's thesis conducted at EPFL. He has taken sabbaticals at Facebook, UC Berkeley, and UCLA. Pradel's research spans software engineering, programming languages, security, and machine learning, with a focus on tools and techniques for building reliable, efficient, and secure software. He is particularly interested in neural-symbolic software analysis, analyzing web applications, dynamic analysis, and test generation. His recent work increasingly incorporates large language models for software engineering tasks including bug detection, test generation, and program repair. His research group has produced significant work in WebAssembly analysis, Python dynamic analysis, quantum program analysis, and automated program repair, with notable tools including Wasm-R3, DynaPyt, LintQ, and RepairAgent. Ernst-Denert Software Engineering Award Emmy Noether grant by the German Research Foundation (DFG) (1.3 million Euro) ERC Starting Grant (1.5 million Euro) Best/Distinguished Paper Awards at FSE (3x), ISSTA, ASE, ASPLOS, and MSR ACM Distinguished Member Multiple ACM SIGSOFT Distinguished Paper and Artifact Awards Pradel has advised numerous PhD students including Matteo (thesis on "Testing and Analysis of Quantum Software"), Luca (thesis on "Supporting Software Evolution via Search and Prediction"), and Daniel (thesis on program analysis for WebAssembly). His research has been supported by significant grants including the Emmy Noether grant and ERC Starting Grant. He serves in leadership roles for major conferences including PC co-chair for FSE 2027 and area chair for ICSE 2026. Pradel leads the Software Lab at the University of Stuttgart, which includes visiting professors Cristian Cadar and Prem Devanbu (both supported by Humboldt Research Awards). The lab maintains active collaborations with institutions including CMU, Google, KAIST, and USI Lugano, and regularly contributes to major software engineering conferences.

Jongmoon BaikView profile

Professor

• software process modeling
• software economics
• software reliability engineering
• +1 more

Jongmoon Baik serves as a Professor at the Korea Advanced Institute of Science and Technology (KAIST), School of Computing, South Korea, and maintains active membership in IEEE and ACM professional organizations. His academic foundation includes a PhD in Computer Science from the University of Southern California, establishing expertise in rigorous software engineering methodologies. Professor Baik's research program centers on software process modeling, software economics, software reliability engineering, and software Six Sigma, with emphasis on quantifying development efficiency and product quality through statistical process control. His work bridges theoretical modeling with industrial application in software lifecycle optimization. Current research trajectories demonstrate growing integration of security analysis into process modeling, exemplified by his upcoming ASE 2025 publication on vulnerability detection using modifiability signals at the line-level code analysis. Professional recognition includes sustained engagement with premier software engineering venues through conference participation and publications. He advises graduate researchers within KAIST's School of Computing and leads the Software Process Research Laboratory (SPIRAL), identifiable through his institutional web presence at spiral.kaist.ac.kr, focusing on empirical studies of software development economics and reliability metrics.

Aurélien SaussayView profile

Assistant Professor

• Environmental Economics
• Climate Change Economics
• Macroeconomics
• +4 more

Aurélien Saussay is an Assistant Professorial Research Fellow at the London School of Economics' Grantham Research Institute, specializing in environmental economics. He currently holds a Leverhulme Early Career Fellowship (2022-2025) and will be visiting Harvard Kennedy School in Fall 2024. His primary research focuses on the interaction between economic inequality and climate change mitigation policies, aiming to address social and political acceptance challenges that hinder effective decarbonization. His research interests span Environmental Economics, Empirical Econometrics, Machine Learning, Natural Language Processing, and Macroeconomics. Saussay employs empirical methods to estimate the impacts of climate change mitigation on economic agents, with the goal of improving decarbonization policy design. His work often combines economic modeling with data science techniques to analyze climate policy impacts. His recent publications reveal a strong focus on understanding the distributional impacts of climate policies, green job transitions, and international dimensions of environmental policy. He has developed sophisticated methodologies using firm-level data, job advertisements, and macroeconomic modeling to assess how climate policies affect different economic sectors and social groups. His research demonstrates particular expertise in analyzing the ThreeME model (Multi-sector Macroeconomic Model for the Evaluation of Environmental and Energy policies), which is used across multiple countries. Scientific Awards: Leverhulme Early Career Fellowship (2022-2025) Saussay previously worked as an economist at OFCE, Sciences Po, where he led the environmental economics team. He remains an associate researcher there and is one of the main co-authors of the ThreeME model, which is used extensively in France, the Netherlands, Mexico, and Indonesia. His work on climate policy visualization, including the Climate Nightmares project that used AI to interpret IPCC reports, demonstrates his commitment to making climate research accessible to broader audiences. His upcoming visiting position at Harvard Kennedy School further highlights his standing in the environmental economics research community.

• Neuroscience
• Toxicology
• Sensory Systems
• +2 more

Dr. Christine Hucke serves as a Scientific Associate within the Neurotoxicology and Chemosensation research group at the Leibniz Research Centre for Working Environment and Human Factors (IFADO) in Dortmund, Germany. Her position falls under the Toxicology department led by Prof. Dr. Jan G. Hengstler, where she conducts experimental research on chemosensory neural processing with a focus on occupational and environmental exposure contexts. She maintains an active role in the institute's scientific operations through her specialized expertise in neurotoxicology. Her research spans Neuroscience, Toxicology, and Sensory Systems with concentrated investigation into olfactory-trigeminal interactions, multisensory integration, and neural coding of complex odors. Employing advanced methodologies including EEG microstate analysis, fNIRS, and behavioral paradigms, she examines spatiotemporal brain dynamics during odor processing. Current projects explore how visual contexts modulate wood odor perception, neural requirements for bimodal odor localization, and conceptual processing of natural odors—addressing both fundamental neurobiological mechanisms and applied environmental health questions. Analysis of her 2018-2025 publication record reveals progressive specialization in ecologically valid chemosensory paradigms, evolving from basic neural mapping of odor lateralization toward real-world applications in indoor air quality assessment and nature-relatedness scale validation. This trajectory demonstrates increasing interdisciplinary integration across neuroscience, environmental psychology, and occupational health, with consistent methodological innovation in multimodal neuroimaging approaches. No scientific awards or major honors were documented in the provided institutional materials. While Dr. Hucke's collaborative publication record indicates extensive research partnerships, the available information does not specify formal PhD or Master's student supervision. Her work appears supported through institutional resources and departmental collaborations rather than individually secured grant funding, though she contributes significantly to IFADO's third-party funded research initiatives as evidenced by her co-authorship on multiple projects. As a core member of the Neurotoxicology and Chemosensation team, Dr. Hucke operates within IFADO's specialized research infrastructure including bioimaging facilities and electrophysiology laboratories. Her work directly supports the institute's mission to investigate human responses to environmental exposures, particularly through advancing methodologies for assessing chemosensory function in occupational settings and contributing to evidence-based exposure guidelines.

Soha HusseinView profile

Assistant Professor

• program analysis
• fuzzing
• symbolic execution
• +4 more

Soha Hussein is an Assistant Professor at Ain Shams University in Egypt. She is actively involved in the software engineering research community, serving in various roles including Publication Co-chair for ASE 2025 and Posters/Tools/SRC Co-Chair for SPLASH/ISSTA 2026. Dr. Hussein received her Ph.D. and M.S. in Computer Science from the University of Minnesota. She also completed the Early Career Teaching and Learning program and the Prepare Future Faculty program at the University of Minnesota, demonstrating her commitment to both research and education. Her research focuses on enhancing software reliability and security through advanced program analysis techniques. Specifically, she specializes in symbolic execution, fuzzing, formal verification, and software testing. Her work addresses the critical challenge of path explosion in symbolic execution through innovative path-merging approaches. Dr. Hussein's research has practical applications in verifying Java programs and improving automated program verification techniques, with recent work extending to AWS Lambda applications. Her publications demonstrate a consistent trajectory of advancing verification techniques while maintaining strong theoretical foundations. Dr. Hussein has received multiple prestigious awards for her work on Java Ranger, including Gold Medals at SV-COMP 2020, 2021, and 2025, as well as a Bronze Medal at SV-COMP 2020. These awards recognize the effectiveness of her path-merging approach in reducing path explosion and improving verification efficiency. She is actively involved in mentoring students, particularly through the Google Summer of Code program where she serves as an organization admin and mentor for the Java Pathfinder (JPF) Organization. Dr. Hussein encourages excellent students to reach out for research opportunities in her lab, having successfully mentored students like Kunha Kim who accepted a Master scholarship at University of Colorado Boulder. Dr. Hussein leads research related to Java Ranger, an extension to Symbolic Pathfinder that implements path-merging techniques for Java verification. Her team continues to advance this work, with recent developments supporting string and array operations and applying the technology to AWS Lambda applications. She maintains active contributions to the SymbolicPathFinder/jpf-symbc repository on GitHub, demonstrating her commitment to open-source verification tools.

Chunyang ChenView profile

Professor

• Software Engineering
• Applied Data Analysis
• Deep Learning
• +5 more

Chunyang Chen is a Professor at the School of CIT, Technical University of Munich (TUM), Germany. He maintains an active research profile in software engineering with a focus on applying data analysis and artificial intelligence techniques to software development processes. His primary research interests include Software Engineering , Applied Data Analysis , Deep Learning , and Human-Computer Interaction . Dr. Chen is particularly known for his work in applying data analysis to automated software development and mining software repositories, with significant contributions in mobile application testing, GUI testing, and leveraging Large Language Models for software engineering tasks. His recent publications (2024-2026) demonstrate a strong focus on integrating Large Language Models with software testing methodologies, particularly for mobile applications. The research trends show increasing sophistication in using multimodal LLMs, dynamic memory techniques, and retrieval augmentation to improve automated testing capabilities. Dr. Chen maintains an active presence in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and ESEC/FSE. He has also delivered keynote speeches on mobile application testing with Large Language Models, highlighting his thought leadership in this emerging area.

Phil McMinnView profile

Professor

• Software Testing
• Search-Based Software Engineering
• Flaky Tests
• +5 more

Phil McMinn is a Professor of Software Engineering at the University of Sheffield, UK, where he leads the Testing Research Group, one of the largest software testing groups in the UK. His research focuses on developing automated techniques for software testing to help developers maintain robust test suites that effectively find bugs. His primary research interests include Software Testing , Search-Based Software Engineering , Flaky Tests , Mutation Analysis , and Pseudo-Tested Code . Recently, his work has centered on helping developers detect and mitigate flaky software tests, as well as developing mutation analysis approaches to assess test suite quality. His research has been funded by the EPSRC and Meta. Analysis of his recent publications (2023-2025) reveals a strong focus on addressing critical challenges in software testing, particularly around test flakiness, pseudo-tested code detection, and test suite optimization. His work spans both theoretical foundations and practical tool development, with significant contributions to understanding the limitations of current testing practices and developing novel approaches to improve test reliability and effectiveness. Professor McMinn serves as an associate editor for the Software Testing, Verification and Reliability journal and has supervised ten PhD students to completion as first supervisor. He currently mentors five PhD students and a post-doctoral researcher on projects related to test flakiness, mutation analysis, and testing for autonomous systems. He teaches the first-year COM1001 Introduction to Software Engineering module and the third-year COM3529 Software Testing and Analysis module at the University of Sheffield, emphasizing team-based development, automated testing, and code quality improvement through refactoring.