Find Academic Supervisors

• Software Testing
• Real-Time Systems
• Embedded Software
• +3 more

Wafa Hasanain is a Contract Instructor at Carleton University's School of Computer Science (SCE), within the Faculty of Engineering and Design. She holds a Ph.D. and focuses on software testing methodologies, real-time embedded systems, and automated testing frameworks. Her research emphasizes clone detection in industrial test code, state-based online testing, and software verification using tools like SPIN Model Checker and RTEdge frameworks. Education: Ph.D. (exact discipline unspecified) Research Interests: Hasanain’s work bridges software engineering and embedded systems. She explores automation in testing complex systems, leveraging clone detection to improve test code maintainability and applying formal verification techniques to real-time software. Her methodologies aim to enhance software reliability through systematic analysis and tool-driven validation. Article Trends: Her publications (2013–2020) concentrate on testing challenges in real-time embedded systems and industrial test code. Key themes include state-based approaches, clone analysis for code maintenance, and integration of model checkers like SPIN with frameworks like RTEdge. These contributions address scalability and accuracy in automated testing environments. Awards & Grants: No scientific awards or grants explicitly mentioned in the text. Advising & Labs: No formal advisees or labs listed, though her research may involve collaborations with teams focused on embedded systems testing.

Zadia CodabuxView profile

Associate Professor

• Empirical Software Engineering
• Technical Debt
• Software Metrics
• +8 more

Zadia Codabux is an Associate Professor at the University of Saskatchewan , Department of Computer Science. Her work focuses on Empirical Software Engineering , Technical Debt , and Software Security . Education: Ph.D. in Computer Science, Mississippi State University (USA) M.S. in Computer Science, University of Mauritius Her research explores technical debt in code reviews, predictive analytics for vulnerability detection, and the intersection of software metrics with security. Recent work includes serverless smell detection tools and studies on LLM impacts on programming platforms. Key publications span conferences like FSE , ESEM , and MSR , with trends showing increasing focus on Android security , code review civility , and developer support systems . Scientific Awards: NSERC Discovery Grant (PI 2021-2026, 2026-2027) IEEE Computer Society TCSE Distinguished Paper Award (SANER'22) SSHRC Insight Grant (collaborator 2024-2025) She supervises PhD and MSc students in software quality research and contributes to equity initiatives like CRA-WP and Grace Hopper Celebration.

Giuliano AntoniolView profile

Associate Professor

• Software Engineering
• Software Evolution and Maintenance
• Reverse Engineering
• +5 more

Giuliano Antoniol is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He is a member of the Institute for Data Valorization (IVADO) and has established himself as a prominent researcher in software engineering, with recognition as one of the world's most productive software engineering researchers and among the top 2% most cited researchers in his field. Professor Antoniol's research spans several key areas in software engineering, with a particular focus on software evolution and maintenance, reverse engineering, static code analysis, and empirical software studies. His work frequently addresses practical challenges in software development, including software quality assurance, technical debt identification, and the application of machine learning techniques to software engineering problems. His research group has produced significant contributions to understanding code smells, identifier naming practices, and the impact of programming language features on software quality. His recent publications reveal a strong trend toward the intersection of traditional software engineering with artificial intelligence and machine learning. Many of his 2023-2025 publications focus on the challenges of testing and verifying machine learning systems, analyzing bugs in AI-generated code, and applying search-based techniques to complex software engineering problems. His work demonstrates a consistent empirical approach, with numerous studies analyzing real-world software systems and developer practices. Ranked among the world's most productive software engineering researchers (2021) Among the top 2% most cited researchers in his field (2021) Award for Most Influential Article of the Decade in Software Engineering (2021) Professor Antoniol has supervised an impressive number of graduate students, with 14 completed PhD theses and 18 Master's theses to his credit. His students have explored diverse topics including software testing, technical debt, machine learning applications in software engineering, and code quality analysis. His research has been supported by numerous grants that have enabled extensive empirical studies and the development of innovative software engineering tools and techniques.

• Software Engineering
• Software Testing
• Machine Learning Systems
• +4 more

Dr. Iftekhar Ahmed is an Associate Professor in the Department of Informatics at the University of California, Irvine’s Donald Bren School of Information & Computer Sciences. His research focuses on software engineering methodologies, emphasizing software testing, analysis, and accessibility. He holds a Ph.D. in Computer Science from Oregon State University (2018) and a B.S. in Computer Science and Engineering from Shahjalal University of Science and Technology, Bangladesh (2007). Education: Ph.D., Computer Science, Oregon State University, 2018 B.S., Computer Science and Engineering, Shahjalal University of Science and Technology, Bangladesh, 2007 His research integrates artificial intelligence, data mining, and empirical software engineering to improve software quality and safety. Key areas include: Automated testing frameworks for large systems Bug prediction models for code vulnerabilities Accessibility testing tools (e.g., Ma11y mutation framework) Safety-critical systems like autonomous vehicles Recent work explores AI-driven code generation, prompt engineering, and mitigating biases in software tools. His team received a $1.2M grant in 2022 to enhance accessibility testing tools. Ahmed also investigates human factors in software development, including developer mental health and tool adoption challenges. His publications address critical issues like code smells in quantum computing, commit message quality, and Jupyter notebook bug patterns. He actively participates in industry-academia collaborations, such as the 2023 Southern California Software Engineering Symposium.

Donghwan ShinView profile

Lecturer

• Mutation Testing
• ML-enabled Cyber-Physical Systems
• Log Analysis
• +3 more

Dr. Donghwan Shin is a Lecturer in Software Testing at the School of Computer Science, University of Sheffield since 2022. He holds a BSc (2006-2010), MSc (2010-2012), and PhD (2012-2018) from KAIST, South Korea. Prior to joining Sheffield, he was a Research Associate (2018-2020) and Research Scientist (2020-2022) at SnT, University of Luxembourg. Education PhD in Computer Science - KAIST (2012-2018) MSc in Computer Science - KAIST (2010-2012) BSc in Information Technology - KAIST (2006-2010) Research Interests His research focuses on mutation testing, testing for ML-enabled cyber-physical systems (e.g., automated driving systems), and log analysis including model inference and anomaly detection. He develops novel testing methodologies for complex AI systems and reliability engineering solutions. Publication Trends Recent publications demonstrate a strong focus on testing machine learning systems, autonomous driving validation, log-based failure prediction, and novel testing methodologies. His work consistently appears in top venues including ICSE, ICST, and IEEE Transactions on Software Engineering. Grants & Projects PI for UKRI grant: "SimpliFaiS: Simplification of Failure Scenarios for Machine Learning-enabled Autonomous Systems" (2024-2026, £464,344) Professional Activities Leads research in the Software Testing Lab and collaborates with industry partners on testing autonomous systems. Serves as School Student Experience Lead.

Apostolos ZarrasView profile

Professor

• Software Architecture
• Software Design
• Software Evolution
• +7 more

Apostolos Zarras is a Professor in the Department of Computer Science & Engineering at the University of Ioannina, Greece. He has been with the institution since 2002, progressing from Visiting Professor to full Professor in 2022. His academic journey includes a Ph.D. from the University of Rennes 1, France (2000), an M.Sc. from the University of Crete (1996), and a B.Sc. from the same institution (1994). Education: Ph.D. in Computer Science, University of Rennes 1, France (1996–1999) M.Sc. in Computer Science, University of Crete, Greece (1994–1996) B.Sc. in Computer Science, University of Crete, Greece (1990–1994) His research centers on software engineering, with a focus on software architecture, design patterns, software evolution, middleware, and service-oriented computing. He explores quality aspects of software systems, including refactoring, schema evolution, and dependable systems. His work often bridges theoretical models with practical applications in distributed and pervasive environments. The recent publications reflect a strong trend in software evolution, particularly schema evolution in databases, refactoring patterns, and the empirical study of design patterns (e.g., GoF patterns) in real-world systems. Themes such as anti-patterns, tool support for refactoring, and naming conventions in SQL are recurrent, indicating a deep interest in improving software quality through structured design and evolution practices. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: Apostolos Zarras has supervised over 17 B.Sc. theses, more than 12 M.Sc. theses, and one Ph.D. thesis. He has served as a principal investigator in several EU and national R&D projects, including the ForeverSOA INRIA project and the CHOReOS FP7 ICT IP project. His professional service includes extensive reviewing for top journals (IEEE TSE, CACM, etc.) and participation in program committees of major conferences (ICSE, FSE, Middleware, ER). Labs and Teams: He has collaborated with the ARLES Group at INRIA Rocquencourt, France, during multiple visiting researcher stays (2005–2008). His research is conducted within the Department of Computer Science & Engineering at the University of Ioannina, where he contributes to graduate studies and academic dissemination.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +3 more

Luís Fernández Sanz is a Full Professor at the Department of Computer Science, Universidad de Alcalá (UVAH), Spain. He holds a Doctorate from the Universidad del País Vasco (1997) with a thesis on software design measurement in formal methodologies. His research focuses on Information Technologies for Training and Knowledge (TIFyC group), emphasizing cybersecurity, AI applications, e-learning accessibility, and sustainable computing. He has supervised research projects on software quality, smart cities, and IT professional frameworks. Education: PhD in Computer Science, Universidad del País Vasco (1997) Research Interests: His work spans cybersecurity frameworks, AI-driven threat detection, mobile application optimization, and educational technology accessibility. He also explores the societal impacts of digital pollution and sustainable IoT practices. Recent studies include blockchain in banking and professional skill alignment with industry demands. Publications: Over 50+ peer-reviewed articles since 2011, focusing on cybersecurity resiliency, smart city workforce analysis, and e-learning infrastructure. Notable contributions include RONSI (network security ROI) and the GREENCO APP for digital pollution awareness. Labs/Teams: Leads the TIFyC research group, collaborating on EU-funded projects like DEDALUS (data literacy) and Skills Match (ICT competencies mapping).

Eray TüzünView profile

Associate Professor

• Software Analytics
• Mining Software Repositories
• Empirical Software Engineering
• +3 more

Eray Tüzün is an Associate Professor of Computer Engineering at Bilkent University, leading the BILSEN Research Group focused on Software Engineering and Data Analytics. He holds a PhD in Information Systems alongside bachelor's and master's degrees in Computer Science. With over 20 years of industry experience, he previously worked at Havelsan, Microsoft, Howard Hughes Medical Institute, and CWRU Genomics Center in roles spanning software engineering and project management. His research emphasizes software analytics, mining software repositories, and empirical software engineering practices. He actively contributes to the International Software Engineering Research Network (ISERN) and holds certifications such as PSM, PSPO, and MCSD. Tüzün advocates for bridging educational and industrial gaps through interactive teaching methods and serious games for developer training. His work on code review practices, traceability tools (e.g., RSTrace+), and bus factor estimation (BFSig, Bus Factor Explorer) highlights practical software engineering challenges. He explores applications of AI in code generation evaluation and process improvement. Tüzün's BILSEN Lab engages in projects like BugCraft (bug reproduction) and SmartDelta (automated QA). He maintains active IEEE and ACM SIGSOFT memberships and contributes to conferences like ICSSP and ICGSE. His educational initiatives include board games (ToolStackers) and plugins for teaching DevOps and version control. Current research trends focus on LLM applications in code review, data cleaning impacts, and developer productivity metrics. Grants and collaborations involve industrial partnerships and national/international research networks. He supervises graduate students in masters/PhD programs and undergraduate volunteers, emphasizing empirical studies and tool development. Future work includes expanding AI-driven software analytics and educational technology integration.

Bruce MaximView profile

Professor

• Engineering Education
• Game Design
• Machine Learning
• +9 more

Dr. Bruce R. Maxim is a Professor at the University of Michigan-Dearborn , affiliated with the College of Engineering and Computer Science and the Dearborn Artificial Intelligence Research (DAIR) Center . He also leads the Search Based Software Engineering (SBSE) Laboratory and co-founded the Game and Multimedia Environment (GAME) Laboratory . Education : PhD, MA, and BS in Computer Science from the University of Michigan, Ann Arbor. Research Interests span Software Engineering , Game Design , Machine Learning , Optimization , and Intelligent Systems . He explores User Interface Design , Virtual Reality , and Social Media in educational contexts, with a focus on Computer Science Education and Knowledge-Based Systems . Recent Publications highlight applications of Multi-Objective Optimization in Web Services and Code Smell Detection , Active Learning Tools in Software Verification , and Service Learning integration with Virtual Worlds . His work bridges Game Design and Software Engineering pedagogy. Awards : UMD Distinguished Digital Education Award (2020) UM Distinguished Faculty Governance Award (2018) Campus Distinguished Teaching Award (2015) Outstanding Young Man of America (1988) Teaching includes coordinating courses like CIS 375 Software Engineering I , CIS 488 Computer Game Design II , and Senior Design Seminars . He emphasizes student projects , peer reviews , and industry-aligned processes for game and software development.

• Software Engineering
• Database Systems
• Static Program Analysis
• +3 more

Csaba Nagy is a researcher specializing in software engineering with expertise in database systems and static code analysis, evidenced by publications from 2015-2022 and an h-index of 13. His work addresses critical challenges in data-intensive software through empirical studies and tool development. His research focuses on SQL code smells, data access technical debt, and database usage evolution in Java applications. Key contributions include identifying patterns of self-admitted technical debt and developing static analysis tools like SQLInspect to detect embedded SQL issues, advancing software quality in database-driven systems. Nagy's publication trend (2016-2022) reveals a cohesive research trajectory examining database anti-patterns through empirical mining of software repositories, with increasing emphasis on practical tooling solutions for industry applications. Scientific Awards: Best Paper Award at the 2016 International Conference on Software Quality, Reliability and Security (QRS 2016) Nagy served as Principal Investigator for a post-doctoral project (2017-2018) supervised by Prof. Cleve, collaborating with researchers including Asmare Muse, Foutse Khomh, and Giuliano Antoniol on database evolution studies. His work demonstrates sustained funding through competitive research grants in software engineering.

Sumon BiswasView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Responsible AI
• +4 more

Sumon Biswas is a tenure-track Assistant Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering. His research focuses on the intersection of Software Engineering and Artificial Intelligence, particularly in modeling, verification, and design of responsible AI systems. He employs both formal and empirical methods to ensure algorithmic fairness and safety in ML pipelines. Education: Postdoctoral Researcher at Carnegie Mellon University PhD in Computer Science from Iowa State University MS in Computer Science from Iowa State University Research Focus: His work centers on Responsible AI Engineering with specific emphasis on fairness verification of neural networks, compositional fairness in ML pipelines, long-term fairness analysis, and safety assurance of predictive systems. He develops techniques using formal verification, causal reasoning, and empirical analysis to address bias in data transformers and ensemble learning. Publication Trends: Biswas consistently publishes in top-tier software engineering venues (ICSE, FSE, ASE) with research evolving from empirical fairness studies to formal verification techniques and pipeline architecture design. Recent works focus on LLM-assisted technical debt management and long-term fairness guarantees in ML systems. Awards & Honors: Research Excellence Award from Iowa State University (2022) ACM SIGSOFT CAPS Grants (2021, 2022) Publication Award from ISU Computer Science Department Distinguished Reviewer for ACM TOSEM Academic Service: Actively serves on program committees for ICSE (2024-2025), ASE (2023-2024), and reviews for IEEE TSE, IEEE Software, and EMSE. Mentors students through ICSE SMeW and judges research competitions. Research Group: Leads a lab focused on Responsible AI Engineering and seeks motivated PhD/MS students to work on verification and design of fair/safe AI systems.

Diana CristeaView profile

Researcher

• Formal Concept Analysis
• Machine Learning
• Data Mining
• +5 more

Diana Cristea is a researcher at the Department of Computer Science within Babeș-Bolyai University , Cluj-Napoca, România. Holding a PhD in Computer Science (2016) from both Babeș-Bolyai University and TU Dresden, her work bridges formal methods, data analysis, and software engineering. She has contributed to academic discourse as an organizer of the 12th International Conference on Formal Concept Analysis (ICFCA) and as a paper reviewer for student scientific sessions. Email: diana.cristea@ubbcluj.ro Education: PhD (2016), Master (2013), and Bachelor (2011) in Computer Science and Mathematics from Babeș-Bolyai University, with PhD research at TU Dresden Her research focuses on Formal Concept Analysis (FCA) , with applications spanning static code analysis , healthcare data visualization , and e-learning platform behavior mining . Key contributions include tools for triadic concept navigation, frameworks for detecting junior developer code issues, and FCA-based models for medical diagnostics and educational trend analysis. She has also explored graph databases for healthcare knowledge discovery and analogical reasoning in electronic health records. Teaching activities include supervising Database courses and thesis coordination, with documented requirements for student attendance, project delivery, and examination processes. She utilizes Microsoft Teams for course management and emphasizes rigorous adherence to deadlines and academic integrity in thesis preparation.

Martin MonperrusView profile

Professor

• Software Engineering
• Program Repair
• Software Supply Chain Security
• +5 more

Martin Monperrus is a Professor of Software Technology at KTH Royal Institute of Technology in Stockholm, Sweden, working within the School of Electrical Engineering and Computer Science, specifically in the Department of Computer Science and Operations Research. He conducts cutting-edge research on software engineering with a focus on program repair, software supply chain security, blockchain technologies, and WebAssembly. His research interests span automated software maintenance, program analysis, software security, and the development of tools to improve software quality and reliability. Monperrus focuses on creating practical solutions for real-world software engineering challenges, particularly in the areas of automated program repair, dependency management, and supply chain security. His work often bridges theoretical concepts with practical applications, resulting in tools and techniques that directly impact software development practices. The publication trends reveal a strong emphasis on modern software challenges including software supply chain security (with works like Dirty-Waters and Software Bills of Materials), program repair evolution (RepairLLaMA and RepairBench), blockchain security (On-Chain Analysis and SoliDiffy), and the application of large language models to software engineering problems. His research has evolved from traditional program analysis to incorporate AI/ML techniques while maintaining a strong foundation in software engineering principles. Monperrus actively collaborates with PhD and Master's students as mentioned on his website where he states he "researches with brilliant PhD and Master's students to do hard, fun and significant experiments on cool topics." His work appears in top software engineering venues and demonstrates consistent productivity with numerous publications each year across multiple research areas. He maintains an active research group (referred to as "my crew" on his website) that focuses on developing practical tools and conducting empirical studies in software engineering. His contact information shows he's based in Office 1524, Level 5 - E-Huset at KTH, with open door policy and preference for email communication.

Petru Florin MihanceaView profile

Associate Professor

• Software Engineering
• Formal Verification and Testing
• Object-Oriented Design
• +5 more

Petru Florin Mihancea is an Associate Professor at the Department of Computers, Faculty of Automation and Computers, Politehnica University of Timisoara. He holds a Dr. Eng. degree and is responsible for teaching Object-Oriented Programming and Software Engineering Fundamentals courses. Dr. Mihancea graduated from "Emanuil Gojdu" Highschool in Oradea, Romania in 1998 and began his software engineering studies at the Politehnica University of Timisoara the same year. In 2002, he won the "Best Software Engineer" award at the first LOOSE competition, which led to him joining the LOOSE Research Group. He defended his Ph.D. Thesis titled "A Novel Client-Driven Perspective on Class Hierarchy Understanding and Quality Assessment" with "Cum Laude" honors in 2009 under the supervision of Dr. Eng. Ioan JURCA. His primary research interests focus on software engineering, particularly in software quality assessment, program analysis, formal verification and testing, object-oriented design, and software maintenance and evolution. Dr. Mihancea has developed various code analysis tools that have been integrated into industrial products, demonstrating the practical impact of his research. His work often bridges the gap between theoretical research and practical applications in software development. Analysis of Dr. Mihancea's recent publications reveals a consistent focus on software quality assessment and program analysis. His research spans from fundamental aspects of object-oriented design to practical tools for detecting code issues and improving software security. A notable trend is his work on creating extensions for commercial analysis tools like CodeSonar and BTC EmbeddedPlatform, showing how his research directly contributes to industry practices. His publications also demonstrate expertise in both theoretical aspects of software engineering and practical tool development. Best Software Engineer award at the first LOOSE competition Dr. Mihancea has been involved in various research projects throughout his career. Notably, as a member of the eAustria Research Institute, he participated in the Spacios project, developing tools for verification of security properties of web applications. He has also worked on hardware research projects, developing tools for circuit customization and generation. His current research focuses on methods and tools for continuous quality assurance in complex software systems. He has collaborated with industry partners to develop code analysis products for automatic detection of potential bugs, demonstrating the practical impact of his research. His work has been acknowledged by PostgreSQL leaders for its contribution to their system. Dr. Mihancea is affiliated with the LOOSE Research Group and has been involved with the eAustria Research Institute. His work often involves developing analysis instruments and program investigation techniques, with several of his research activities being integrated into industrial products.

• Computer Science
• Software Engineering
• Machine Learning
• +6 more

Dario Di Nucci is a Researcher at Joint Academic Data Science (JADS), focusing on Data Analytics and Data Governance . His work intersects Computer Science , Software Engineering , and Machine Learning , with key contributions to DevOps, Infrastructure-as-Code, and Smart Contracts. Recent Publications include a 2025 review on Data Mesh architecture in ACM Computing Surveys , 2024 studies on vulnerability fix mapping in open-source repositories (ACM Transactions) and automated smart contract testing (Science of Computer Programming), and 2022 empirical analyses of linguistic inconsistencies in infrastructure-as-code (Empirical Software Engineering) and defect prediction via process metrics (IEEE Transactions on Software Engineering). These works collectively advance methodologies in code quality, security, and natural language processing. Collaborations span institutions like Vrije Universiteit Brussel and Tilburg University , with media coverage in 2023 highlighting his research in machine learning and cloud computing. He has supervised at least one PhD student (Supervised Work section) and contributed datasets like Detecting Code Smells Using Machine Learning Techniques (2018, Figshare).