جست‌وجوی استاد و پژوهشگر

Chunyang Chenمشاهده پروفایل

استاد

• Software Engineering
• Applied Data Analysis
• Deep Learning
• +۵ مورد دیگر

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.

D. Paul Ralphمشاهده پروفایل

استاد

• Software Engineering
• Research Methods
• Software Project Management
• +۵ مورد دیگر

D. Paul Ralph is a Professor of Software Engineering at Dalhousie University's Faculty of Computer Science in Halifax, Canada, where he directs the Dalhousie Software Engineering Lab (DalSEL). Recognized as one of the world's top 25 software engineering researchers by CSRankings and ScholarGPS, Dr. Ralph has published over 100 peer-reviewed articles in premier venues including IEEE Transactions on Software Engineering and the ACM/IEEE International Conference on Software Engineering. His research program investigates the intersection of software engineering, human-computer interaction, and project management with a focus on improving the environmental, social, economic, and technical sustainability of software development. Dr. Ralph's work has been adopted by leading technology companies including Adobe, Amazon, AT&T, Broadcom, Canon, IBM, Google, HP, Microsoft, Netflix, Oracle, PayPal, Samsung, Salesforce, Yahoo!, and Walmart. He advocates for evidence-based practices and is editor-in-chief of the Empirical Standards for Software Engineering Research. Dr. Ralph's recent publications demonstrate a strong emphasis on methodological rigor in software engineering research, with particular attention to socially sustainable software development, empirical standards, and the implications of emerging work models. His research consistently bridges theoretical foundations with practical applications, challenging opinion-based approaches like SWEBOK and Waterfall models in favor of empirically validated practices. He emphasizes human-centered research with direct participant involvement rather than AI or repository mining approaches. Editor-in-chief of Empirical Standards for Software Engineering Research Ranked among world's top 25 software engineering researchers by CSRankings and ScholarGPS Recipient of multiple research awards Research funding from Google, NSERC, and CIHR As a mentor and educator, Dr. Ralph actively supervises postdoctoral fellows, PhD students, and Master's students at Dalhousie University. His lab maintains a collaborative culture with strong industry connections and prioritizes supporting researchers from underrepresented groups. He is deeply committed to advancing research methodology in software engineering and has contributed significantly to conference programs, serving on committees for ICSE, EASE, ESEC/FSE, and other major venues.

Andreas Vogelsangمشاهده پروفایل

استاد

• Requirements Engineering
• Model-Based Systems Engineering
• Empirical Software Engineering
• +۱ مورد دیگر

Andreas Vogelsang is a full Professor of Software Systems Engineering at the University of Duisburg-Essen and board member of paluno - the Ruhr Institute for Software Technology. His research focuses on requirements engineering, model-based systems engineering, empirical software engineering, and AI for software engineering, with significant contributions to the software engineering community through conference leadership and publications. His research interests center on improving requirements engineering processes using AI techniques, addressing model-based systems engineering challenges in embedded systems, and conducting empirical validation of software engineering practices. He investigates industrial adoption barriers, quality requirements interpretation, and the application of large language models to automate requirements tasks, emphasizing practical relevance and empirical evidence. Recent publications reveal a strong trend toward leveraging AI—particularly large language models—for requirements elicitation, traceability, and code generation. His work bridges theoretical advancements with industrial applications, focusing on empirical validation in domains like automotive systems and research software, while exploring explainability in AI-assisted development. Scientific awards include: Junior Fellow of the German Informatics Society (GI) (2018) Young Scientist of the Year by academics and the DHV (2019) As a board member of paluno, he drives interdisciplinary research initiatives and industry collaborations, fostering innovation in software technology through academic-industrial partnerships and contributing to the institute's strategic direction in software engineering research.

Lili Weiمشاهده پروفایل

استادیار

• Software Analysis
• Software Testing
• Code Repository Mining
• +۶ مورد دیگر

Lili Wei is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. She specializes in software engineering research with a focus on Android applications, smart contracts, and IoT software. Prior to joining McGill, she completed her PhD at HKUST under Prof. S.C. Cheung and was a Postdoctoral Fellow in the CASTLE group at HKUST, receiving the Hong Kong Research Grant Council Postdoctoral Fellowship. Her research interests center on software analysis, testing, and mining code repositories, particularly addressing compatibility issues in Android applications, security vulnerabilities in smart contracts, and testing methodologies for mobile software. She has made significant contributions to understanding and solving fragmentation-induced compatibility issues in Android apps, which has been a persistent challenge for developers due to the diverse ecosystem of Android devices and OS versions. Dr. Wei's publication record shows a consistent trajectory of impactful research in top software engineering venues including ASE, ICSE, FSE, ISSTA, and IEEE Transactions on Software Engineering. Her recent work has expanded into blockchain security with a focus on front-running vulnerabilities in smart contracts. Her research combines empirical studies with practical tool development to address real-world software engineering challenges. Peter Silvester Faculty Research Award in Electrical & Computer Engineering (McGill University) Distinguished Reviewer (ASE 2022) Hong Kong RGC Postdoctoral Fellowship Scheme (2020) ACM SIGSOFT Distinguished Artifact Award (ICSE 2019) Google PhD Fellowship (in Mobile Computing, 2018) Microsoft Research Asia PhD Fellowship (2018) ACM SIGSOFT Distinguished Paper Award (ASE 2016) Dr. Wei has been actively involved in the academic community, serving on program committees for major software engineering conferences including ICSE, ASE, FSE, ISSTA, and MOBILESoft. She has held leadership roles such as Tutorials Track Chair for ASE 2023, PC Chair for MOBILESoft 2023, and is currently seeking PhD students interested in software engineering research, particularly in program analysis, test generation, and mobile computing. Her teaching at McGill includes ECSE 250: Fundamentals of Software Development, ECSE 321: Introduction to Software Engineering, and ECSE 688: Automated Software Testing and Analysis.

Iftekhar Ahmedمشاهده پروفایل

دانشیار

• Software Engineering
• Software Testing
• Machine Learning for Software Engineering
• +۳ مورد دیگر

Iftekhar Ahmed is an Associate Professor in Informatics at the Donald Bren School of Information and Computer Science, University of California, Irvine. His research focuses on software engineering, particularly combining software testing, static analysis, and machine learning to develop better tools and techniques for software quality assurance. His educational background includes: PhD in Computer Science (2018) from Oregon State University, advised by Carlos Jensen BSc in Computer Science & Engineering (2007) from Shahjalal University of Science and Technology Dr. Ahmed's research interests center on software testing, static analysis, and the application of machine learning to software engineering problems. He has made significant contributions to mutation analysis, particularly in scaling this technique for real-world software systems. His work often bridges theoretical advances with practical applications, focusing on how to make software testing more effective and efficient for developers. He leads the STAIRS (Software Engineering & Testing Using Artificial Intelligence for Reliable Software) research group at UCI, where his team explores innovative approaches to software reliability through AI and machine learning. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software engineering practices. There's a clear focus on applying machine learning to code analysis, commit message generation, and bug detection, while maintaining rigorous empirical validation through studies of real-world software projects and developer practices. His work spans multiple domains including web accessibility, quantum computing, and Jupyter notebooks, showing both depth in core software engineering topics and breadth across application areas. Dr. Ahmed has received several prestigious awards: IBM Ph.D. Fellowship for academic year 2016-2017 Graduate School tuition relief Scholarship for academic year 2016-2017 IBM Ph.D. Fellowship for academic year 2017-2018 Actively involved in the academic community, Dr. Ahmed serves on program committees for major software engineering conferences including ASE, ICSE, and ESEC/FSE. He is currently accepting PhD students into his research group and emphasizes mentorship and professional development. His research has been supported by various grants that enable his team to explore innovative approaches to software testing and analysis. Dr. Ahmed leads the STAIRS research group at UCI, which focuses on developing AI-powered techniques for software testing and reliability. The group collaborates with industry partners and academic institutions to ensure their research addresses real-world challenges in software development. Current projects include improving mutation testing scalability, analyzing code smells in emerging domains like quantum computing, and developing tools for accessibility testing.

Coen De Rooverمشاهده پروفایل

استاد

• program analysis
• program transformation
• software engineering
• +۸ مورد دیگر

Coen De Roover is a Professor at the Software Languages Lab (SOFT) of Vrije Universiteit Brussel (VUB) since October 2015, where he leads the Code Analysis and ManiPulation (CAMP) subgroup. His research focuses on program analysis design and its applications to software quality, including soft verification of contracts, incremental abstract interpretation, vulnerability detection in infrastructure code, and mining change patterns in commits. Key Research Areas: static analysis, dynamic analysis, mining software repositories, software engineering tools, security, and empirical studies. Conference Roles: Organizing Committee Chair for ECOOP Academy (2026), Steering Committee Member for GPCE, Program Committee Member for ICFP, SANER, and VMCAI, and Session Chair for multiple research tracks. Notable Contributions: Publications on WebAssembly analysis, Ansible security, concolic testing, and abstract interpretation frameworks. He also serves as Programme Director for the Bachelor in Computer Science at VUB since 2019-2020.

Tomoki Nakamaruمشاهده پروفایل

استادیار

• Domain-Specific Languages
• Mining Software Repositories
• Notebook Programming
• +۵ مورد دیگر

Tomoki Nakamaru is an Assistant Professor at the Department of Multidisciplinary Systems Science, Graduate School of Arts and Sciences, The University of Tokyo. He holds a Ph.D. in Information Science and Engineering from The University of Tokyo, advised by Prof. Shigeru Chiba. Education: PhD (2021), The University of Tokyo M.Sc. (2018), The University of Tokyo B.Eng. (2016), The University of Tokyo His research focuses on Domain-Specific Languages , Mining Software Repositories , and Notebook Programming . He develops tools like Multiverse Notebook and Silverchain to enhance code exploration and API usability. He has published extensively at top venues like OOPSLA and PLDI , covering topics from Fluent Interface Generation to Runtime Monitoring . His 15 most recent works span 2017–2025. Scientific Awards: Yamashita Memorial Research Award (2023) University of Tokyo Business Reform Special Award (2021) JSST Student Encouragement Awards (2019–2020) Distinguished Artifact Award at OOPSLA (2019) Dean's Award, UTokyo (2018) Nakamaru serves on program and artifact committees for conferences like ECOOP , OOPSLA , and ‹Programming› . He teaches courses including Practical Programming and Introduction to Information Systems .

• Programming Education
• Novice Programming
• Educational Tools Development
• +۴ مورد دیگر

Neil Christopher Charles Brown is an Associate Professor in the School of Computing at the University of Kent, where he has been actively contributing to computing education research since completing his PhD in 2011. His work bridges theoretical computer science concepts with practical educational applications, focusing on how novices learn to program and how educational tools can better support this process. Dr. Brown's research interests center on programming education, with specific expertise in novice programming behavior, educational tool development, and computing education research methodologies. His early work focused on programming language theory and concurrency models, but he has increasingly specialized in computing education research, particularly examining how students interact with programming languages and environments. He has made significant contributions to understanding novice Java programming mistakes through large-scale data analysis and has developed frame-based programming environments to ease the transition from block-based to text-based programming. His recent publications (2023-2025) show a strong focus on AI applications in programming education, registered reports methodology in computing education research, and the development of tools like Strype that support novice programmers. Brown has published extensively in top venues including SIGCSE, ICER, and ACM Transactions on Computing Education, often collaborating with Michael Kölling on projects related to BlueJ and educational programming environments. Among his notable contributions is the Blackbox data repository project, which has collected millions of student programming attempts to analyze learning patterns. His 2018 paper 'Ten quick tips for teaching programming' in PLoS Computational Biology has been widely influential in the computing education community. Dr. Brown has served in leadership roles for major computing education conferences, including co-organizing ICER 2025 and contributing to SIGCSE workshops on research methodologies. His work demonstrates a commitment to improving both the science and practice of computing education through rigorous research and practical tool development.

• Software Engineering
• Software Defect Prediction
• Machine Learning for Software Engineering
• +۷ مورد دیگر

Akito Monden is a prolific Japanese software-engineering scholar with 171 publications recorded in dblp during 1995-2025. His recent work centres on defect prediction, online learning, bandit-based tool selection, and empirical studies of software quality and human factors. Research interests span software defect prediction, mining software repositories, effort estimation, clone detection, code generation, and the application of machine-learning techniques (notably bandit algorithms and ensemble methods) to practical software-engineering tasks. He also investigates requirements ambiguity, security-bug identification with large language models, and gaze-based human-computer interaction in programming education. Across 2022-25 articles Monden explores online learning to cope with concept drift in defect datasets, multi-armed bandit algorithms for dynamic selection of clone detectors, fault-localisation techniques and code generators, and LLM-based security-bug detection. These themes reflect a sustained focus on data-driven, adaptive approaches that improve software quality assurance processes.

Yannik Lubasمشاهده پروفایل

پژوهشگر

• Performance Benchmarking
• Microservice Architecture
• Cloud Applications
• +۳ مورد دیگر

Yannik Lubas is a Doctoral Researcher at the Chair of Software Engineering (Computer Science II) at the University of Würzburg, under Prof. Samuel Kounev. He holds a Master's and Bachelor's degree in Computer Science from the same institution. His research focuses on performance benchmarking of microservice applications, machine learning applications in runtime management, and cloud application architectures. He has contributed to frameworks like Creo for generating executable microservice applications and conducted analyses on temporal performance deviations and GitHub-based microservice workloads. Education: M.Sc. (2023), B.Sc. (2021), University of Würzburg His research interests revolve around leveraging automated tools and data-driven approaches to enhance performance evaluation and management in distributed systems. He actively participates in the SPEC RG committee and has received the ASQF Sponsorship Award 2024. Lubas coordinates teaching activities for advanced programming and supervises student theses in software engineering.

Uwe Lämmelمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Science
• Data Mining
• +۳ مورد دیگر

Uwe Lämmel is a Professor at Wismar University of Applied Sciences since 1996, with a focus on Artificial Intelligence and Computer Science . He holds a diploma in mathematics from the University of Rostock (1981) and a Dr.-Ing. from Rostock (1985). His work bridges theoretical foundations with practical applications in fields like knowledge management, education, and business informatics. Education : Mathematics (Diploma) at University of Rostock (1976–1981); Promotion to Dr. Ing. in 1985 Lämmel's research spans Artificial Intelligence , Data Mining , and Knowledge Management , with a particular emphasis on educational frameworks like 'Object First' programming instruction and semantic wiki systems. He has co-authored multiple editions of foundational texts in AI and Data Mining. His recent work (2025) explores the enduring relevance of Gottlob Frege's logic in modern computing. Publications highlight AI deployment in healthcare insurance workflows, semantic web technologies, and pedagogical innovations in programming education.

• Semantic Web
• Knowledge Graphs
• Machine Learning
• +۳ مورد دیگر

Ahmet Soylu is a researcher at Oslo Metropolitan University , Norway, with a focus on Semantic Web , Knowledge Graphs , and Machine Learning applications in Cloud Computing and Smart Manufacturing . His work bridges semantic technologies with data-driven solutions for industrial contexts, particularly in collaboration with Bosch . Key research themes: Knowledge Graph Embeddings, Cloud Cost Optimization, Industrial Data Analytics Collaborations: Dumitru Roman, Evgeny Kharlamov, Radu Prodan Recent publications (2024–2025) explore hyperbolic knowledge graph embeddings, sustainable LLM inference, and cloud storage optimization. These works integrate semantic modeling with ML for edge-cloud systems and industrial data extrapolation. His methodology emphasizes graph-based approaches for cloud cost modeling, microservice scheduling, and AI innovation discovery in open-source repositories. Applications include welding quality monitoring, maritime supply chain optimization, and semantic ML pipelines.

Nathalia Nascimentoمشاهده پروفایل

پژوهشگر ارشد

• Multiagent Systems
• Internet of Things
• Artificial Intelligence
• +۳ مورد دیگر

Nathalia Nascimento is a Research Fellow at Pennsylvania State University, USA, holding a doctorate from Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. Her expertise spans Computer Engineering and Artificial Intelligence with institutional affiliations reflecting active research roles in North American academia. Her educational foundation includes doctoral research at PUC-Rio focused on interdisciplinary computing systems. Research interests prominently feature Multiagent Systems , Internet of Things , Artificial Intelligence , Software Engineering , Software Product Line , and Software Framework development, emphasizing AI integration into adaptable software architectures. Recent publications demonstrate a cohesive trajectory in leveraging large language models for human-AI collaboration, system adaptability, and performance validation. Key trends include architectural innovations for chatbot automation control, self-adaptive multiagent coordination, and rigorous empirical analysis of LLM capabilities in programming contexts, highlighting her focus on practical AI-software synthesis. Dr. Nascimento actively contributes to academic governance through program committees for EASE 2025 (AI Models/Data Track), CASCON 2025 (Posters Track), and Mining Software Repositories 2024 (Mining Challenge), reflecting community engagement without documented advising responsibilities or grant leadership.

Gustavo A. Olivaمشاهده پروفایل

استاد مدعو

• Programmable Blockchains
• Software Ecosystems
• Code Analytics
• +۶ مورد دیگر

Gustavo A. Oliva is an Adjunct Professor at Queen's University in Canada, where he leads the blockchain research team at the Software Analysis and Intelligence Lab (SAIL). His research focuses on enabling cost-effective decentralized applications on programmable blockchain platforms like Ethereum, alongside empirical studies in software ecosystems, code analytics, and explainable AI. Dr. Oliva earned his PhD from the University of São Paulo (USP) in Brazil under Professor Dr. Marco Gerosa. Prior to his current role, he was a Post-Doctoral Fellow at Queen's University supervised by Professor Dr. Ahmed Hassan. His primary research spans programmable blockchains, software ecosystems (particularly npm), code analytics, and explainable AI. He employs static analysis, historical repository mining, and machine learning to investigate software evolution, dependency management, and smart contract development. Current projects address gas efficiency challenges in Ethereum, upgradeability patterns in smart contracts, and the impact of foundation models on software engineering practices. Recent publications reveal a dominant focus on blockchain systems (70% of recent work), with growing emphasis on foundation model challenges (FMware). His Ethereum research explores transaction processing, gas optimization, and technical debt, while newer work catalogs software engineering challenges in trustworthy AI-powered systems. Scientific recognition includes: Microsoft Azure for Research sponsorship Capes/CNPq scholarship for Visiting Research at Queen's University (2014) HPE scholarships for Smart Cities and Cloud Service Choreography projects European Commission FP7 funding for CHOReOS project Dr. Oliva actively mentors 8+ students across academic levels. His PhD advisees include Muhammad Ahasanuzzaman (ongoing), Amir Mohammad Ebrahimi, and Filipe Cogo (now at Huawei). Master's students Michael Pacheco and Ahmad Abdullah Zarir now work at Huawei and Amazon respectively. He also supervises visitor and undergraduate researchers in blockchain projects. His service includes program committees for ICSE, SANER, and MSR conferences, plus tutorial leadership at ASE, KDD, and FSE. As director of SAIL's blockchain research team, he manages projects on Ethereum smart contract analysis, npm dependency ecosystems, and AI-driven software engineering. Current initiatives include SPICE (automated issue labeling) and foundational work on trustworthy FMware development, with industry collaborations at Huawei and Amazon.

Vahid Alizadehمشاهده پروفایل

استادیار

• Empirical Software Engineering
• Software Quality
• Software Refactoring & Maintenance
• +۷ مورد دیگر

Vahid Alizadeh is an Assistant Professor in the College of Computing and Digital Media (CDM) at DePaul University in Chicago, USA, where he has been serving since 2020. His academic career focuses on bridging the gap between software quality research and industrial practice through empirical studies and intelligent tool development. Education: Ph.D. in Computer Science, University of Michigan (2015-2020) M.Sc. in Electrical Engineering, University of Tehran (2011-2013) B.Sc. in Electrical Engineering, Iran University of Science and Technology (2006-2011) Research Interests: Dr. Alizadeh's research spans empirical software engineering, software quality, refactoring & maintenance, intelligent software engineering, and AI-enabled systems. His work combines rigorous empirical methods with practical tool development to address real-world software engineering challenges. He has particular expertise in developing intelligent refactoring technologies that are currently deployed by organizations impacting thousands of programmers worldwide. Research Trends: His publication record shows a consistent focus on applying AI and machine learning techniques to software engineering problems, particularly in the areas of refactoring and quality assurance. Recent work demonstrates increasing integration of AI/ML approaches with traditional software engineering practices, reflecting the broader trend toward AI4SE (AI for Software Engineering). Awards & Recognition: Excellence in Teaching Award from DePaul University (2025) $67,000 DePaul-RFUMS Grant for AI-Powered Community Pharmacy Research (2024) ACM SIGSOFT Distinguished Paper Award at MODELS Conference (2020) Distinguished University of Michigan-Dearborn Honors Scholar (2020) Outstanding Graduate Research Assistant Award (2018/2019) Professional Activities: Dr. Alizadeh has served in various capacities at major software engineering conferences including ASE 2022 as Session Chair and Virtualization Co-Chair, and MODELS 2025 as Web Co-Chair. His work has resulted in one patent and multiple invention licenses through the University of Michigan Tech Transfer Office. He actively mentors graduate students interested in empirical software engineering, software quality, and intelligent refactoring technologies.