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Austin MordahlView profile

Assistant Professor

• Static Analysis
• Taint Analysis
• Automated Testing
• +4 more

Austin Mordahl is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research focuses on improving the usability and reliability of software quality assurance tools, particularly static analyses and fuzz testing. Dr. Mordahl actively seeks students interested in cutting-edge research in software engineering at UIC. Dr. Mordahl's research interests span multiple areas of software engineering with a particular emphasis on: Static Analysis and Taint Analysis for software security Automated Testing techniques including fuzz testing Improving the usability and reliability of software quality assurance tools Configurable static analysis tools and their behavior Applying machine learning to triage and configure static analysis tools Lifting static analysis to work on software product lines Improving evaluations of fuzz testing tools His recent publications demonstrate a strong focus on addressing challenges in configurable static analysis tools, with particular attention to nondeterministic behavior, configuration spaces, and automatic testing and debugging approaches. Mordahl's work bridges theoretical foundations with practical applications, aiming to make software quality assurance tools more accessible and effective for developers. Dr. Mordahl has received several prestigious awards for his work: NSF Graduate Research Fellowship Awardee (2020) Eugene McDermott Graduate Research Fellowship Awardee (2020) ICSE 2019 Student Research Competition Winner Dr. Mordahl is actively involved in mentoring and seeks students interested in software engineering research. He has served on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and PLDI. His service to the academic community extends to artifact evaluation committees, demonstrating his commitment to research reproducibility and rigor.

Stefan WagnerView profile

Professor

• Software Quality
• AI-Supported Software Engineering
• Empirical Studies
• +3 more

Stefan Wagner is a full professor of software engineering at the Technical University of Munich (TUM), where he leads the Chair of Software Engineering within the Informatics Heilbronn 3 department. He earned his PhD in computer science from TUM and has established himself as a leading researcher in software engineering with a unique interdisciplinary background combining computer science and psychology. Dr. Wagner's research spans multiple areas of software engineering including software quality, human factors, AI-based software engineering, automotive software, and empirical studies. His work often bridges theoretical foundations with practical applications, particularly in the automotive domain and AI-assisted development. He has published over 130 peer-reviewed scientific articles and authored the book 'Software Product Quality Control.' His recent publications demonstrate a growing focus on AI integration in software engineering, with particular attention to large language models, empirical validation of AI-assisted development practices, and the human aspects of these emerging technologies. There's also a strong thread of research on automotive software systems, technical debt, and software quality measurement. 2022 Class of IEEE Computer Society Distinguished Contributors Senior member of IEEE and ACM Best Full Paper Award, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement 2020 Best Paper Award, EAI International Conference on Cognitive Computing and Cyber Physical Systems 2020 Keynote at the International Conference on Software Quality 2020 Google Research Award 2009 Dr. Wagner actively mentors students and early-career researchers, with several frequent co-authors on his publications likely representing his advisees. His research has been supported by various funding sources enabling his work on automotive software, AI in software engineering, and empirical studies. He serves on the editorial boards of IEEE Software, Empirical Software Engineering, and the Journal of Systems and Software, contributing significantly to the academic community. His research group appears to focus on several key areas including AI-assisted software development, automotive software systems, and empirical software engineering methods. The group maintains active collaborations with industry partners, particularly in the automotive sector, and engages in interdisciplinary research bridging computer science with psychology and human factors.

Yutian TangView profile

Assistant Professor

• AI+SE
• Large-language Model+SE
• Program Analysis
• +5 more

Dr. Yutian Tang serves as an Assistant Professor (UK Lecturer) and Principal Investigator at the School of Computing Science, University of Glasgow, where he supervises PhD students and leads research in AI-driven software engineering. His academic journey includes a PhD from The Hong Kong Polytechnic University's Department of Computing. His research spans AI+SE integration , particularly focusing on Large Language Models for program analysis, software testing, and Android security. Key areas include: LLM-assisted vulnerability detection and repair Empirical studies of real-world software systems Privacy protection mechanisms Configuration compatibility in mobile applications Smart contract security optimization His publication portfolio shows a clear trajectory toward AI-augmented software engineering , with recent work demonstrating how LLMs can enhance taint analysis, binary code similarity detection, and test generation. This evolution reflects the field's broader shift toward AI integration while maintaining rigorous empirical validation. Award highlights include: Best Industry Paper Award at ISSRE'18 Elevation to IEEE Senior Member (2024) Three Android OS defects confirmed by Google Security Team As an active researcher and community contributor, Tang serves on 40+ program committees including PLDI, ICSE, and FSE. His work receives funding from National Natural Science Foundation of China, Shanghai Science Commission, OpenAI, and Google. Current projects focus on automated bug localization and LLM-based testing frameworks, with recent grants from OpenAI Cybersecurity and Google Cloud programs. He leads research groups investigating Android security and AI-assisted program analysis, collaborating with institutions like Lund University.

Leopoldo TeixeiraView profile

Assistant Professor

• Software Engineering
• Software Product Lines
• Configurable Systems
• +7 more

Leopoldo Teixeira is an Assistant Professor at the Informatics Center (CIn) of the Federal University of Pernambuco, where he leads the Software Testing and Analysis Research group. He is also affiliated with the Software Productivity Group and CIn-Trust. Since May 2023, he has served as head of graduate studies at his department. His educational background includes: PhD in Computer Science from the Federal University of Pernambuco (CIn-UFPE) in 2014, supervised by Paulo Borba and Rohit Gheyi MSc in Computer Science from CIn-UFPE in 2010 Bachelor's degree in Computer Engineering from the Polytechnic School of Pernambuco in 2007 During his PhD, he spent a winter term at the University of Waterloo working with Krzysztof Czarnecki. In 2022, he was a CAPES-Alexander von Humboldt Experienced Research Fellow at the Chair of Software Engineering of Universität des Saarlandes, working with Sven Apel on variability analysis over time and space. His research focuses on Software Engineering with emphasis on improving software quality and productivity. His work spans software product lines, configurable systems, refactoring, formal methods, software testing, and mobile development. His publication record shows a consistent focus on software analysis, testing, and maintenance, with recent work exploring Dockerfile repair and flaky test detection. His scientific recognition includes the CAPES-Alexander von Humboldt Experienced Research Fellowship in 2022. As an active member of the software engineering research community, he has served on program committees for numerous conferences including ASE, ICSE, ESEC/FSE, and others across multiple years. Leopoldo leads the Software Testing and Analysis Research group at CIn-UFPE and is affiliated with the Software Productivity Group and CIn-Trust, indicating a strong institutional presence and collaborative research environment.

Alicia García HolgadoView profile

Associate Professor

• Knowledge Management
• Educational Technology
• Digital Ecosystems
• +5 more

Alicia García-Holgado is an Associate Professor in the Department of Computer Science and Automation at the University of Salamanca. She is a member of the GRIAL Research Group, where she leads the "Social Responsibility and Inclusion" research line, and also affiliated with the Salamanca Biomedical Research Institute and the University Institute of Educational Sciences. Her educational background includes a Degree in Computer Sciences (2011), M.Sc. in Intelligent Systems (2013), and a PhD in Education in the Knowledge Society (2018, Cum Laude), all from the University of Salamanca. Dr. García-Holgado's research focuses on developing technological ecosystems for knowledge and learning management in diverse contexts, with particular emphasis on promoting diversity and inclusion in STEM fields. She has organized numerous international activities aimed at reducing the gender gap in STEM, including the "Women Researchers' Breakfast at the University of Salamanca," which received the Gutenberg Award for scientific dissemination in 2020. Her work bridges computer science, education, and social responsibility, with practical applications in academic, professional, and public administration contexts. Her publication record shows a strong focus on educational technology, knowledge management systems, and informal learning recognition, with over 170 international publications in scientific journals and conferences. Her research spans multiple disciplines including computer science, education, and human-computer interaction, with applications in academic, professional, and public administration contexts. Gutenberg Award for scientific dissemination 2020 by CoxmoCaixa Dr. García-Holgado has participated in over 50 research projects including MIH, VALS, WYRED (H2020), and W-STEM. She currently coordinates the Erasmus+ SPADATAS project focused on security and privacy in academic data management. She serves on the Negotiating Committee of the II Equality Plan of the University of Salamanca and is active in several professional organizations including IEEE, ACM, and AMIT. She is Associate Editor of the IEEE Revista Iberoamericana de Tecnologías del Aprendizaje and the Revista Facultad de Ingeniería. As a member of the GRIAL Research Group, she contributes to the group's work on interactive systems for learning, educational technologies, and strategic knowledge management, while leading the "Social Responsibility and Inclusion" research line that focuses on gender equity in STEM fields.

• Software Engineering
• Machine Learning
• Information Retrieval
• +3 more

Ana Cristina Marcén Terraza serves as Professor and Director of the Artificial Intelligence degree at the University of San Jorge's School of Architecture and Technology. She teaches core courses across Computer Engineering, Video Game Design and Development, and Bioinformatics programs including Information Systems, Operating Systems Administration, and Human-Computer Interaction. Education: Doctorate in Computer Engineering from Polytechnic University of Valencia (Thesis: Fragment recovery in models through machine learning techniques) Research Focus: Her work centers on applying Machine Learning to solve critical challenges in Software Engineering , particularly for information retrieval systems, model-driven development frameworks, and automated software maintenance processes. This research bridges artificial intelligence with practical software development methodologies. Research Leadership: Actively leads the National Project "ALPS" (Intelligent Evolutionary Assistants for Software Product Lines) and National Project "DataME" (Model-Driven Software Production for Big Data Applications). Previously contributed to the International Project "REVaMP2" (Round-trip Engineering and Variability Management). Research Affiliation: Member of "Software Variability for Internet of Things" Research Group Publication Record: Author of multiple high-impact publications in premier journals including Information & Software Technology (IST), Journal of Systems and Software (JSS), and Software and Systems Modeling (SoSyM).

Stephen P. JohnstonView profile

Professor

• Polymer Processing
• Injection Molding
• Process Control
• +4 more

Stephen P. Johnston is a Professor in the Department of Plastics Engineering at the University of Massachusetts Lowell's Francis College of Engineering. He holds a B.S. in Plastics Engineering Technology from Pennsylvania State University, an M.S. in Plastics Engineering (2005), and a Ph.D. in Plastics Engineering (2007) from UMass Lowell. His industrial experience includes positions at Moldflow Corp., Bausch & Lomb, and Lord Corp. Dr. Johnston's research focuses on polymer processing and product design with specialization in: Injection molding process monitoring, control, and development Computer-aided engineering for part and mold design Process instrumentation and finite element analysis Industrial applications of polymer processing technologies His publication portfolio demonstrates consistent focus on injection molding optimization, sensor-based process control, and polymer characterization. Recent work explores capillary rheometry, microstructured surfaces, and thermally conductive polymers. Significant Awards: Teaching Excellence Awards (2014, 2011) Faculty Member of the Year (2013) Outstanding Paper Award from SPE (2011) Multiple graduate research awards (2004-2006) He leads industrially-funded research supported by over 25 companies, resulting in 40+ publications, a book chapter, and three patents. Johnston advises the Student Chapter of the Society of Plastics Engineers and teaches courses including Mold Design Engineering, Product Design, and Introduction to Engineering.