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Wesley Klewerton Guez AssuncaoView profile

Associate Professor

• Software Modernization
• Variability Management
• Software Quality
• +7 more

Wesley Klewerton Guez Assuncao serves as an Associate Professor in the Department of Computer Science within the College of Engineering at North Carolina State University. Previously, he held positions as a University Assistant/Senior Researcher at Johannes Kepler University Linz in Austria, Postdoctoral Researcher at Pontifical Catholic University of Rio de Janeiro in Brazil, and Assistant/Associate Professor at Federal University of Technology - Paraná in Brazil. His academic journey includes a Ph.D. in Computer Science from the Federal University of Paraná with a visiting period at Johannes Kepler University. Dr. Assuncao's research spans several critical areas in modern software engineering. His primary interests include Software Modernization (reverse engineering, re-engineering, and migration), Variability Management (variability mechanisms, software customization, and software reuse), and Software Quality (technical debt, code smells, and software refactoring). He also investigates Model-Driven Engineering (model inconsistency detection, repair generation, and change propagation), Collaboration in Systems Engineering (change synchronization, tool flexibility, and conflict awareness), Software Testing (regression testing, integration testing, and test case selection/prioritization), and AI4SE (Generative AI, Machine Learning, and Evolutionary Algorithms for Software Engineering). His recent publications reveal a strong focus on software modernization challenges, particularly regarding legacy systems transformation, microservices architecture, and the application of AI techniques in software engineering. The research shows increasing integration of Large Language Models in addressing software evolution problems, with emphasis on empirical validation through industrial collaborations. His work consistently bridges theoretical foundations with practical applications, as evidenced by multiple industry partnerships and real-world case studies. Dr. Assuncao has received numerous prestigious awards including Distinguished Reviewer Awards from FSE and SANER conferences, a Young Researcher Award from Johannes Kepler University, and multiple Best Paper Awards from top software engineering conferences. His research has been recognized with ACM SIGSOFT Distinguished Paper Awards and IEEE Computer Society TCSE Distinguished Paper Awards. In terms of academic service, he serves as Co-editor of the In Practice track at the Journal of Systems and Software and has held various leadership roles in major conferences including ICSE, SANER, MSR, and SPLC. He has successfully secured substantial research funding totaling approximately USD 1.91 million from sources including the Austrian Science Fund, Brazilian National Council for Scientific and Technological Development, and state-level Brazilian research foundations. Dr. Assuncao leads the Wolfpack Innovations in Software Engineering Research (WISER) Lab at NC State University, where he supervises graduate students working on cutting-edge software engineering research problems. His lab maintains strong collaborations with international institutions and industry partners including Dynatrace and ITPRO Consulting & Software GmbH.

Mauro CaporuscioView profile

Professor

• Self-adaptive systems
• Cyber-Physical Systems
• Decentralized control
• +5 more

Mauro Caporuscio is a Professor of Computer Science at Linnaeus University's Department of Computer Science and Media Technology , affiliated with the Faculty of Technology . He holds a PhD from the University of L'Aquila (2006), with prior academic appointments as Assistant Professor at Politecnico di Milano (2010-2014) and Postdoctoral Researcher at INRIA Paris-Rocquencourt (2006-2009). Research Groups : Cyber-Physical Systems (CPS), Engineering Resilient Systems (EReS), Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), Smart Industry Group (SIG) Ongoing Projects : Digital Twin of Organizations (DTO), Smart Industry Education, Aladino (architecture methodologies), SmartDat (industrial digitalization) His research focuses on applying Model-Based Software Engineering to Self-Adaptive Systems , emphasizing decentralization and resilience . Key areas include Digital Twin for organizational modeling, Cyber-Physical Systems , and IoT-enabled platforms with applications in robotics and physical rehabilitation. Publications span IEEE Transactions , Springer series, and top software engineering conferences. He contributes to program committees and has developed educational programs including a Master's in Computer Science with software engineering specialization.

Daniel FriedView profile

Assistant Professor

• Natural Language Processing
• Code Generation
• Grounded Language Interaction
• +3 more

Daniel Fried is an Assistant Professor at the Language Technologies Institute within the School of Computer Science at Carnegie Mellon University. His research bridges natural language processing, pragmatics, and human-computer interaction, with a focus on making programming more communicative through language models. He previously held postdoctoral positions at FAIR Seattle and the University of Washington, earned a PhD from UC Berkeley, M.Phil from Cambridge, and B.S from the University of Arizona. University: Carnegie Mellon University Department: Language Technologies Institute Ranks: Assistant Professor Research Interests: Daniel Fried's work centers on grounded interaction, where language models perform tasks in real-world contexts. His key domains include code generation (e.g., InCoder, StarCoder), multi-agent pragmatic modeling, and multimodal dialogue systems. He explores how agents can infer user intent through language, enhance code synthesis via reinforcement learning, and improve factuality in embeddings. Recent Article Trends: His 2025 publications emphasize multi-agent coordination, programmatic skill induction, and execution-based code benchmarks. Work like RepoST and Agent Workflow Memory highlights scalable repository-level environments and memory mechanisms for agentic tasks. Papers such as CRScore and Dynamic Coalition Structure Detection reflect his focus on pragmatic evaluation and strategic language modeling. Scientific Awards: Okawa Research Grant (2023), Best Paper Award at Deep Learning for Code Workshop (2023), Best Paper Award at CVPR (2012) Advising: He mentors PhD students at CMU, including Andre He, JY Koh, and Zora Wang. His alumni advisees, like Alex Xie, have contributed to machine learning and education projects.

Karthik ShivashankarView profile

Research Fellow

• Software Engineering
• Machine Learning
• Natural Language Processing
• +3 more

Karthik Shivashankar is a Doctoral Research Fellow in Software Engineering at the University of Oslo's Department of Informatics. His research develops transformer-based NLP methodologies to enhance software engineering processes, focusing on technical debt identification, issue classification, and maintainability optimization. Investigation domains include machine learning applications for task prioritization in development workflows, anti-pattern detection in ML systems, and scalable maintenance solutions. Recent publications demonstrate novel approaches to automating software quality assessment through issue tracker analysis and repository mining. Shivashankar contributes to the Software Engineering (SE) research group with cross-disciplinary applications in educational policy analysis and healthcare AI. His scholarly outputs reflect consistent emphasis on bridging NLP capabilities with practical software engineering challenges.

Md Tajmilur RahmanView profile

Assistant Professor

• Software Engineering
• AI in Software Engineering
• Software Maintenance and Evolution
• +3 more

Md Tajmilur Rahman is an Assistant Professor in the Department of Computer and Information Science at Gannon University , Erie, Pennsylvania, USA. His research focuses on software maintenance, evolution, architecture , and the application of AI and Large Language Models to improve software development practices. He has contributed to understanding feature toggle architectures and their impact on software systems. Education : PhD in Software Engineering from Concordia University (2018), supervised by Dr. Peter Rigby. Research Interests : Software Maintenance, Feature Toggles, Release Engineering, Software Architecture, AI in Software Engineering. Teaching : Courses include Software Testing, Software Maintenance, Software Architecture, Data Analysis, and Mobile Application Development at both undergraduate and graduate levels. Professional Service : Organizing Co-Chair for ACM Student Research Competition at ICSE 2025, Program Committee member for ESEC/FSE 2022, and Chair of IEEE Erie Section (2022-2024). Community Involvement : Strategic Planning Committee member at L’Arche Erie. Publications highlight empirical studies on feature toggles in Google Chromium, predictive analysis of software quality, and tool development for automated user story generation. His work bridges academic research with industry practices, particularly in release engineering and architectural growth control. Industry Experience includes senior software engineering roles at Morgan Stanley and PBSC Urban Solutions, focusing on back-end development, CI/CD implementation, and architectural decision-making.

Fausto FASANOView profile

Associate Professor

• Mobile Security
• Software Engineering
• Formal Methods
• +5 more

Prof. Fausto FASANO is an Associate Professor at the University of Molise, affiliated with the Department of Bioscienze e Territorio. He specializes in cybersecurity, software engineering, and formal methods for mobile systems. His research focuses on Android security, privacy indicators, malware detection, and data anomaly analysis. He has published extensively on topics such as permission models, runtime verification, and biomedical informatics applications. His academic work spans over two decades, with contributions in software quality management, code smell analysis, and formal methods for security. Notable areas include dynamic approaches to defusing logic bombs in Android, empirical studies on privacy indicators, and the application of LLMs in medical diagnostics. Prof. FASANO's publications reflect a blend of theoretical and applied research, addressing challenges in both technical (e.g., mobile security, formal verification) and interdisciplinary domains (e.g., biomedical essential oils analysis). His work emphasizes practical tools like RPCDroid for permission context identification and ADAMS for artifact management systems. He has collaborated on projects involving traceability recovery in software systems and the integration of formal methods into security protocols. His research also touches on education, including blended learning strategies and software engineering pedagogy.

• Software Engineering
• Empirical Software Engineering
• Metamorphic Testing
• +3 more

Dietmar Pfahl is a Professor affiliated with the University of Tartu, Estonia, and the University of Calgary, Canada. His work bridges academia and industry through empirical software engineering, metamorphic testing, and agile methodologies. Key Roles: Editor/Author in conferences like PROFES, ICST, MOBILESoft. Current Affiliation: University of Tartu (Estonia) and University of Calgary (Canada). Research Interests focus on metamorphic testing , automated driving system safety , dependency network analysis , and agile software development . Recent work includes: Metamorphic test pipelines for optimization software. Dependency ecosystem impacts of package managers. LLM-based feature-sentiment extraction from app reviews. Scientific Contributions span over 219 publications since 1994, with 2025 entries addressing: Safety testing frameworks for automated driving. Test data-driven metamorphic relation selection. Industry experiments on test-driven development. His awards and leadership include organizing PROFES conference proceedings and advocating for software freedom.

Paula RachowView profile

Researcher

• Software Engineering
• Computer Science
• Software Quality
• +3 more

Paula Rachow is a Researcher at the Department of Informatics , Faculty of Mathematics, Informatics and Natural Sciences (MIN) at the University of Hamburg. She works within the Software Engineering and Construction Methods research group under Prof. Dr.-Ing. Matthias Riebisch. Her primary responsibilities include doctoral research and teaching activities in software development modules. Employment period: February 2018 - January 2025 Office location: Building D, Room 232 Primary focus: Architecture smells, technical debt management, and software quality Research Focus : Paula's research investigates the intersection of software architecture quality and practical development challenges. Key areas include: Architecture smells and maintainability taxonomy Technical debt prevention frameworks (e.g., TAP framework) Refactoring decision support systems Empirical studies of clean code adoption barriers Architecture-centric software engineering methods Agile project management quality integration Teaching Activities : Summer 2023: Exercises for Software Development II Winter 2022/23: Exercises for Software Development I Continous teaching involvement since 2018 Experience with both traditional and digital assessment methods Academic Background : M.Sc. in Computer Science, University of Hamburg (2014-2018) B.Sc. in Computer Science, University of Hamburg (2011-2014) Student Teaching Assistant for Theoretical Foundations of Computer Science (2012-2014)

Shin NagayamaView profile

Associate Professor

• Neuroscience
• Olfactory System
• Neuronal Network Modules
• +2 more

Shin Nagayama, PhD is an Associate Professor at The University of Texas Health Science Center at Houston (UTHealth Houston) . His research focuses on neuronal network modules and olfactory system functionality , specifically examining how individual neuronal activities are processed within these modules. His research interests include: Neuronal network modules In vivo electroporation neuronal labeling Two-photon functional imaging Odor coding and smell perception Information processing in neuronal circuits From the article trends , we observe: Recent focus on colorectal cancer microenvironments and genetic associations Long-term research in neuronal organization of olfactory bulb circuits Key methodologies include spatial transcriptomics , single-cell analysis , and genetic mapping

Peter HegedusView profile

Assistant Professor

• Software Engineering
• Empirical Software Studies
• Static Code Analysis
• +4 more

Peter Hegedus is an Assistant Professor at the Software Engineering Department of the University of Szeged and a researcher at FrontEndART Ltd. His work bridges academic research with practical software development through teaching, empirical studies, and application development projects. Research Focus: He specializes in software engineering practices, with emphasis on empirical evaluation of code quality, static analysis techniques, and machine learning applications in software development. His research integrates big data methodologies, refactoring validation, and tool development for software maintainability. Academic Contributions: His recent publications highlight dataset creation for defect prediction, ML-based alarm filtering systems, and framework designs for machine learning model interaction. These works demonstrate expertise in both theoretical and applied aspects of software engineering. Teaching Responsibilities: He leads lectures and labs for courses on Enterprise Information Systems, Big Data, and software development practices, incorporating practical assignments like creating REST API testing tools and permission-based image browsers.

Stefano LambiaseView profile

Researcher

• Social and Human Aspects of Software Engineering
• Cultural and Geographical Dispersion
• Community Smells
• +6 more

Stefano Lambiase is a researcher in Computer Science currently affiliated with Aalborg University in Denmark, where he holds an institutional email address (stla@cs.aau.dk). He earned his PhD in Computer Science from the University of Salerno at the Software Engineering Laboratory (SESA Lab), with a doctoral project titled "Longitudinal Data-Driven Software Project Management" focusing on the social and human aspects of Software Development and Management. His educational background includes: PhD in Computer Science from University of Salerno (2019-present) Master's Degree in Computer Science from University of Salerno (2019-2021), with thesis on "Cultural and Geographical Dispersion Impact on Communication and Collaboration of Software Development Teams" Bachelor's Degree in Computer Science from University of Salerno (2015-2019), with thesis on "Test Smell Detection and Suggestions (TESEUS)" Stefano's research interests center around the human and social dimensions of software engineering , with particular focus on cultural and geographical dispersion in software development teams, community smells (socio-technical anti-patterns), and their impact on productivity and software quality. His work bridges theoretical frameworks with practical applications, developing tools like CADOCS (a conversational agent for community smell detection) and DARTS (a test smell detection and refactoring plugin). He has also explored emerging areas including quantum software communities, metaverse development, and the integration of large language models in software engineering practices. His publication record demonstrates a consistent focus on socio-technical aspects of software development, with recent work examining cultural dispersion effects, community smells in various contexts (including machine learning systems and quantum computing), and educational applications in the metaverse. His research employs diverse methodologies including empirical studies, surveys, interviews, and tool development. Stefano has received recognition for his work, including a Best Paper Award at the 25th Euromicro Conference on Software Engineering and Advanced Application (SEAA 2022) for his paper "'There and Back Again?' On the Influence of Software Community Dispersion Over Productivity". He has been actively involved in the software engineering research community, serving on program committees for major conferences including ICSE-SEET 2024, SANER 2024, ICSEA 2023, MSR 2023, and SCAM-NIER 2023. Additionally, he served as Social Media and Web Chair for the Third Workshop on Gender Equality, Diversity, and Inclusion in Software Engineering (GE@ICSE 2022). Stefano has developed several research tools and projects: DARTS (Detection And Refactoring of Test Smells): An IntelliJ plugin for detecting and refactoring test smells CADOCS : A conversational agent for community smell detection and refactoring in Slack Erwin Bot : A chat bot for management support built with Azure Cloud Services BiblioNet : A Spring Boot Web App for library support QuantuMoonLight : A low-code platform to experiment with quantum machine learning SENEM : A software engineering-enabled educational metaverse SERGE : A serious game for risk management education in software project management

• Software Engineering
• Microservices Architecture
• Performance Engineering
• +4 more

Andrea Janes is a Professor at the Free University of Bozen-Bolzano, Department of Computer Science. She specializes in software engineering, with a focus on microservices architecture, performance engineering, and agile methodologies. Her work bridges theoretical research and practical applications, addressing challenges in software maintainability, scalability, and developer productivity. Her research interests span microservices architecture design, technical debt management, and sensor-based systems in sports technology. She has organized major conferences like ESOCC 2025 and PROFES 2023, reflecting her leadership in academic and industrial collaborations. Recent projects include empirical studies on microservice ownership, organizational coupling in open-source projects, and software aging detection in virtual networks. Publications from 2025 highlight her contributions to anti-pattern correlation in microservices, rejuvenation strategies, and developer collaboration dynamics. Her work on impermanent identifiers (2024) enhances source code comprehension, while sensor-enhanced quickdraws for climbing (2023-2024) demonstrate cross-disciplinary innovation. Janes has been cited over 117 times and actively contributes to workshops on performance engineering (WEPPE25) and gender equality in software engineering (GE@ICSE). Her research emphasizes practical tools like CATTO for test case selection and PPTAM for scalability assessment.

Antonio MastropaoloView profile

Assistant Professor

• Software Engineering
• AI for Software Engineering
• Sustainable AI
• +4 more

Dr. Antonio Mastropaolo is an Assistant Professor in the Department of Computer Science at William & Mary, part of the College of Arts & Sciences. He holds a PhD from Università della Svizzera italiana (USI) in Switzerland and earned his B.S. in Computer Science and M.S. in Software Systems Security from the University of Molise, Italy. His academic journey reflects a strong international background with research experience in both European and North American contexts. B.S., Computer Science, University of Molise, Italy M.S., Software Systems Security, University of Molise, Italy Ph.D., Computer Science, Università della Svizzera italiana (USI), Switzerland His research centers on Software Engineering , with a focus on automating software development practices such as bug fixing, program repair, and code refactoring using artificial intelligence. He explores how AI can enhance software quality, maintainability, and sustainability. A key theme in his work is Sustainable & Efficient AI in Software Engineering , aiming to reduce the environmental footprint of software systems through intelligent optimization techniques. The 15 most recent articles reflect a consistent trajectory in AI-driven automation for software development , with emphasis on empirical evaluation, industrial applicability, and environmental impact. These publications span top venues like ICSE, ASE, and TSE, covering subfields including automated program repair, green coding, large language models for code, self-healing systems, and sustainability metrics. The research combines machine learning, empirical methods, and practical tool development to address real-world software engineering challenges. Dr. Mastropaolo has presented his work at leading software engineering conferences, indicating recognition within the research community. While no specific awards are listed, publication in premier forums such as the International Conference on Software Engineering (ICSE) and Transactions on Software Engineering (TSE) signifies high-impact contributions. He is actively engaged in research and mentoring, though no formal advisees are currently listed. His work likely involves collaboration with graduate students and participation in externally funded research projects, given the nature of his field and publication record. Future directions may include expanding the integration of AI into full software lifecycles and advancing metrics for sustainable software development. Dr. Mastropaolo contributes to the growing strength of William & Mary’s Computer Science program in intelligent systems and software engineering. His research aligns with emerging trends in autonomous development environments and environmentally conscious computing, positioning him at the forefront of next-generation software engineering innovation.

Denys PoshyvanykView profile

Professor

• Software Engineering
• Software Analytics
• Program Comprehension
• +10 more

Denys Poshyvanyk is the Chancellor Professor of Computer Science and Graduate Director at William & Mary, where he leads the SEMERU research group. He received his Ph.D. from Wayne State University under Dr. Andrian Marcus. Research Interests: Software Engineering (SE), with a focus on software analytics, evolution, and maintenance Deep Learning for Software Engineering (DL4SE) and SE for deep learning (SE4DL) Program comprehension, mobile app testing and security, reverse engineering Large-scale mining of software repositories and traceability His research integrates AI and machine learning to improve software development practices, with a strong emphasis on empirical studies and tool development. Recent work explores large language models (LLMs), GUI testing, and quantum-classical software integration. Awards and Honors: NSF CAREER Award (2013) IEEE Fellow and ACM Distinguished Member Multiple Best Paper and ACM SIGSOFT Distinguished Paper Awards Most Influential Paper Awards (ICSME, ICPC, SCAM, MSR) Service and Leadership: Senior Associate Editor, ACM TOSEM Editorial Board, Empirical Software Engineering, JSEP, SCP Steering Committee: FSE, ASE, AIware PC Co-chair for FSE’25, MOBILESoft'24, ASE'21; General Chair for MOBILESoft'24 Member, CRA-E Board of Directors He has advised numerous students who have secured faculty and research positions. His work is widely cited and has significantly influenced the software engineering community through both theoretical contributions and practical tools.

• Empirical software engineering
• Software quality
• Software evolution
• +7 more

Aiko Yamashita is an Associate Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, Department of Computer Science. Her research focuses on empirical software engineering, particularly software quality, evolution, and human/organizational factors in computing. She actively collaborates with international researchers and contributes to software engineering literature through publications in top-tier venues like IEEE and ACM. Empirical Software Engineering Code Smell Detection Technical Debt Analysis Human Factors in Software Development Software Maintenance Open Source Systems Her recent publications explore connections between code smells and maintenance effort, analyze developer discussions on technical debt, and advance methodologies for empirical case studies. She works with colleagues from institutions like Politecnico di Milano and McGill University, applying mixed-method approaches combining screen recordings, metrics, and expert reviews. Aiko participates in research groups including Applied Artificial Intelligence and Universal Design of ICT (UD-ICT). Her work bridges academic research with industry practice, focusing on actionable insights for software quality improvement. Contact her at aiko.yamashita@oslomet.no for collaborations or further information.