جستجوی استاد راهنمای دانشگاهی

Tien N. Nguyenمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۹ مورد دیگر

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.

Lars Grunskeمشاهده پروفایل

استاد

• Software Engineering
• Formal Methods
• Model Checking
• +۵ مورد دیگر

Lars Grunske is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. His research focuses on software and systems engineering, safety-critical systems, and software evolution. Department: Computer Science University: Humboldt University of Berlin Academic Rank: Professor His work spans automated software analysis, probabilistic model checking, and formal methods for complex systems. Key collaborations include researchers from Swinburne University, University of Hull, and University of Queensland. Recent publications address research software engineering, program repair, and explainability in cyberphysical systems. Professional roles include leadership in examination boards and program committees for conferences like ICSE and ASE. Contact info: Email: grunskel@hu-berlin.de Phone: +49 30 2093-41142 Address: Unter den Linden 6, Berlin

Wing Lamمشاهده پروفایل

استادیار

• Software Testing
• Flaky Tests
• Mobile Testing
• +۴ مورد دیگر

Wing Lam is an Assistant Professor at George Mason University specializing in software engineering with a focus on software testing methodologies. His academic service includes program committee membership for major conferences including ASE, ICSE, ISSTA, and ESEC/FSE, as well as session chair roles across multiple tracks. His research interests center on flaky tests , mobile application testing , and continuous development optimization . Lam's work addresses critical challenges in test reliability, particularly order-dependent flaky tests and resource-related flakiness. His research bridges theoretical foundations with practical applications in modern software development pipelines, with recent expansion into AI-assisted testing methodologies. Lam's publication record shows a clear trajectory focusing on flaky test detection and mitigation, with approximately 60% of his recent work dedicated to various aspects of this problem. His research increasingly incorporates machine learning techniques for UI testing and test optimization, reflecting broader trends in the field. The consistent publication venue pattern across top software engineering conferences indicates strong recognition within the academic community. Lam has served in multiple leadership roles including Program Co-Chair for MOBILESoft Research Track and Committee Member for ASE's New Ideas and Emerging Results (NIER) Track. His involvement in workshops focused on flaky testing demonstrates his specialization in this niche area of software testing.

Darko Marinovمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Regression Test Prioritization
• +۴ مورد دیگر

Darko Marinov is a Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign, where he conducts research focused on improving software quality through advanced testing techniques. His work addresses fundamental challenges in software testing including flaky tests, regression test prioritization, and configuration testing. Dr. Marinov's research interests span multiple areas of Software Engineering , with particular emphasis on: Software Testing and Quality Assurance Regression Test Prioritization and Selection Flaky and Non-Deterministic Tests Configuration and Variability Testing Test Suite Optimization Software Reliability and Defect Detection His extensive publication record demonstrates consistent contributions to the field, with research trends showing evolution from foundational testing techniques to addressing modern challenges in software testing, particularly focusing on reliability issues in continuous integration environments and the growing complexity of test suites in large software systems. Dr. Marinov has received numerous prestigious awards recognizing the impact of his work: Two ACM SIGSOFT Impact Paper awards (2012, 2019) ASE Most Influential Paper Award (2015) FSE Test-of-Time Honorable Mention (2024) Seven ACM SIGSOFT Distinguished Paper awards CHI Best Paper Award (2017) Elected Fellow of Automated Software Engineering (2023) His research has been generously supported by major technology companies and government agencies including NSF, Boeing, Facebook, Google, Huawei, IBM, Intel, Microsoft, Qualcomm, Samsung, and SRC. Dr. Marinov actively contributes to the academic community through conference organization, serving as Area Chair for ASE 2025 and Program Committee member for multiple conferences including FSE 2026. His mentoring excellence has been recognized through various awards, highlighting his commitment to guiding the next generation of software engineering researchers.

Reyhaneh Jabbarvandمشاهده پروفایل

استادیار

• Neural Program Analysis
• Software Testing
• Bug Localization
• +۴ مورد دیگر

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Intelligent CAT Lab. Her research focuses on improving software quality, reliability, and maintenance through neuro-symbolic approaches that combine AI techniques with formal methods. Her research interests span Neural Program Analysis, Software Testing (with emphasis on mobile apps and autonomous software), Bug Localization, and Applied Optimization for Software Analysis. She has made significant contributions to the fields of energy testing for Android applications, neuro-symbolic approaches for code analysis, and large language models for software engineering tasks. Dr. Jabbarvand's recent publications reveal strong trends in applying machine learning to software engineering problems, particularly using neuro-symbolic methods to bridge the gap between deep learning and formal program analysis. Her work on code translation, test flakiness, and test oracle generation demonstrates her focus on practical applications of AI in software development workflows. Google PhD Fellowship in Programming Technology and Software Engineering Rising Star in EECS NSF CAREER Award Dr. Jabbarvand has received research funding from multiple sources including NSF, IBM Research, and C3.ai. She actively mentors students through her Intelligent CAT Lab and has served on numerous program committees for major software engineering conferences including ICSE, FSE, and ISSTA. She teaches courses on Advanced Topics in Software Engineering, ML for Code, and Software Engineering I. Her lab focuses on neuro-symbolic approaches to software engineering problems, bringing together PhD, undergraduate, and high school students to tackle challenges in AI-assisted software development and testing.

• Software Engineering
• Machine Learning
• DevOps
• +۳ مورد دیگر

Emelie Engström is a software engineering researcher at Lund University, specializing in empirical studies at the intersection of academia and industry. Her work focuses on regression testing, machine learning applications in software quality, DevOps practices, and aligning requirements engineering with autonomous systems validation. University: Lund University Research Areas: Software Engineering, Machine Learning, DevOps, Autonomous Systems Her recent research examines ML-driven bug report classification, sustainable DevOps for cyber-physical systems, and optimizing anomaly detection through industrial case studies. She has developed taxonomies like SERP-test to improve communication between academic researchers and software practitioners. Key article trends show strong emphasis on: 2025: ML techniques for bug classification 2024: Ericsson case studies on automated bug assignment 2023: Anomaly detection in DevOps 2021: Autonomous driving system testing As a prolific collaborator, she works with researchers across Sweden including Per Runeson, Markus Borg, and Kai Petersen on projects ranging from cognitive load analysis in large-scale development to regression test scoping in software product lines.

• Software Performance Engineering
• Microservice Architectures
• Cyber-Physical Systems
• +۳ مورد دیگر

Catia Trubiani is a faculty member at the Gran Sasso Science Institute in L'Aquila, Italy, specializing in software performance engineering, architectural analysis, and cyber-physical systems. Her work bridges theoretical modeling with practical applications, focusing on performance antipatterns, uncertainty quantification, and DevOps practices. In research , she explores performance modeling of microservices, federated learning systems, and cyber-physical systems, with a strong emphasis on architectural decision-making under uncertainty. Her recent articles analyze performance regression testing, aging detection in networks, and anti-pattern correlation in distributed systems, reflecting her interest in robust, scalable software solutions. She collaborates extensively with researchers like Raffaela Mirandola , Alberto Avritzer , and Riccardo Pinciroli , contributing to tools and frameworks such as PLUS (Performance Learning for Uncertainty of Software) and VisArch (Visualisation of Performance-Based Architectural Refactorings). Her work has been published in journals including IEEE Transactions on Software Engineering and Future Generation Computer Systems , as well as conferences like ICSE, ECSA, and ICPE.

Aitor Arrietaمشاهده پروفایل

مدرس

• Software Engineering
• Software Testing
• Cyber-Physical Systems
• +۴ مورد دیگر

Dr. Aitor Arrieta is a permanent full-time Lecturer and Researcher at Mondragon University in Spain. His research focuses on software engineering and testing methodologies for complex systems including Cyber-Physical Systems, AI-based systems, and Generative AI models. He maintains strong industry collaborations with companies like Orona and Developair to address real-world engineering challenges. Research interests span: AI system validation and safety testing Metamorphic testing techniques Autonomous vehicle verification Large Language Model bias/fairness analysis Search-based software engineering His recent publications demonstrate a focus on developing automated testing tools for AI systems, particularly in safety-critical domains. Awarded Best Paper at the 15th International Symposium on Search-Based Software Engineering. Active in European research projects: InnoGuard: Generative AI for autonomous cyber-physical systems TRUST4AI: Trustable AI-driven internet search

Ajay Jhaمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Software Maintenance
• +۴ مورد دیگر

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

Dan Haoمشاهده پروفایل

استاد

• software testing
• debugging
• program comprehension
• +۷ مورد دیگر

Professor Dan Hao is a distinguished faculty member at the Institute of Software, School of Computer Science, Peking University, where he has established himself as a leading researcher in software engineering. His extensive service to the academic community includes membership on the Steering Committee for The International Conference on Automated Software Engineering (ASE) since 2021, The ACM SIGSOFT International Symposium on Software Testing and Analysis since 2025, and The International Systems and Software Product Line Conference (SPLC) from 2018-2022. He has served as Program Co-Chair for multiple major conferences including ISSTA 2027, ICSME 2025, ICST 2023, SANER 2022, and ASE 2021. Professor Hao received his Bachelor's degree from Harbin Institute of Technology in 2002 and completed his Ph.D. at Peking University in 2008, followed by post-doctoral research at the same institution until 2009. His academic journey reflects a deep commitment to advancing software engineering research and education in China. Professor Hao's research primarily focuses on software testing and debugging, program comprehension, and software maintenance. His work has significantly contributed to compiler testing, fault localization, regression testing, and automated program repair. He has pioneered approaches in compiler auto-tuning, test-case prioritization, and history-guided testing techniques. His research bridges theoretical foundations with practical applications, addressing real-world challenges in large-scale software systems, particularly in online service environments. His publication record demonstrates a consistent trajectory of high-impact research in top-tier software engineering venues. Professor Hao's work shows increasing integration of machine learning techniques with traditional software engineering problems, particularly evident in his recent publications on LLM applications for code generation, neural theorem proving, and contrastive learning for vulnerability detection. His research maintains strong connections between theoretical rigor and practical applicability in industrial settings. ACM SIGSOFT Distinguished Paper Award for PDCAT: Preference-Driven Compiler Auto-Tuning at FSE 2025 Distinguished Paper Award for Formalizing, Mechanizing, and Verifying Class-Based Refinement Types at ECOOP 2024 ACM SIGSOFT Distinguished Paper Award for History-Guided Configuration Diversification for Compiler Test-Program Generation at ASE 2019 ACM SIGSOFT Distinguished Paper Award for History-driven Build Failure Fixing: How Far Are We? at ISSTA 2019 As an advisor, Professor Hao has mentored numerous graduate students, currently supervising 9 Ph.D. students and 7 Master's students. His former students have gone on to prestigious positions at institutions including King's College London, Tianjin University, Fudan University, and major technology companies like Huawei and China Construction Bank. His academic leadership extends through editorial roles as Deputy Editor-in-Chief of Software Testing, Verification and Reliability (STVR) and membership on the editorial boards of several premier journals including ACM Transactions on Software Engineering and Methodology, ACM Computing Surveys, and Empirical Software Engineering. Professor Hao leads a vibrant research group at Peking University's Institute of Software, focusing on cutting-edge problems at the intersection of traditional software engineering and artificial intelligence. His team actively collaborates with both academic institutions and industry partners to address practical challenges in software development and maintenance processes.

August Shiمشاهده پروفایل

استادیار

• Software Testing
• Regression Testing
• Flaky Tests
• +۳ مورد دیگر

August Shi is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin. His research focuses on software testing, particularly regression testing, with emphasis on improving reliability with respect to flaky tests and increasing testing speed without compromising quality. Dr. Shi obtained his PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2020. Prior to that, he earned a B.S. in both Computer Science and Electrical and Computer Engineering from The University of Texas at Austin in 2013. Dr. Shi's research interests center on software testing and regression testing , with a particular focus on addressing challenges related to flaky tests . His work aims to make regression testing both more reliable (by tackling issues with flaky tests) and faster (without sacrificing testing quality). His research spans multiple aspects of software testing including test prioritization, test scheduling, flaky test detection and repair, and optimization of continuous development processes. Dr. Shi's publication record shows a consistent focus on flaky tests and regression testing across multiple top-tier software engineering conferences including ASE, ICSE, ISSTA, and ESEC/FSE. His research has evolved from foundational work on test suite reduction and mutant generation to more recent innovations in flaky test classification, debugging, and repair. A notable trend is his increasing application of machine learning techniques to testing problems, particularly in his 2024-2025 publications. Dr. Shi actively contributes to the software engineering research community through committee service on major conferences including ASE, ICSE, ISSTA, and ESEC/FSE, where he has served on program committees for Research Papers, NIER tracks, and Tool Demonstration tracks. Dr. Shi is currently seeking PhD students to work on projects related to his research interests in software testing. He has supervised or co-supervised multiple student projects presented at major software engineering conferences, demonstrating his commitment to mentoring the next generation of researchers.

• Software Testing
• Regression Testing
• Flaky Tests
• +۳ مورد دیگر

Fabian Leinen serves as a Researcher at the Department of Software and Systems Engineering, Technical University of Munich, working under Prof. Alexander Pretschner's chair. His research focuses on critical challenges in modern software development pipelines, particularly addressing flaky tests in continuous integration environments. His research interests center on software testing reliability , with specialization in flaky test identification , resource-constrained test execution , and automotive software validation . Leinen investigates how system resource limitations impact test determinism and develops methodologies to reduce flakiness-related costs in industrial CI/CD workflows. His work bridges theoretical testing frameworks with practical industrial applications, particularly in safety-critical domains. Notable recognition includes the Rohde & Schwarz Best Bachelor Award for thesis work on dynamic program analysis for flaky test detection. His research outputs demonstrate strong industry relevance through multiple industrial case studies and automotive software applications. Leinen actively supervises academic theses, having guided multiple Master's and Bachelor's projects on flaky test repair, detection optimization, and root cause analysis. His teaching portfolio includes regular instruction in Software Quality Seminars and Advanced Topics of Software Engineering across multiple semesters. Current research directions focus on severity-aware test prioritization and resource-efficient flakiness mitigation strategies.

• Search Based Software Engineering
• Software Testing
• Evolutionary Computation
• +۷ مورد دیگر

Giovani Guizzo is a Software Engineer at Kii working as a Blockchain Engineer and Front-end developer with React, while maintaining an active research profile in Search-Based Software Engineering. He earned his PhD in Computer Science from the Department of Informatics at Federal University of Paraná (UFPR) in Brazil under Professor Silvia Regina Vergilio in 2018. His academic collaborations include significant affiliations with University College London where he has published as a researcher. His research program centers on applying evolutionary computation and search-based techniques to software engineering challenges, particularly in software testing and optimization. Guizzo's work bridges theoretical advances in multi-objective optimization with practical software engineering applications, resulting in innovative solutions for test model inference, mutant reduction strategies, and automated program repair. His approach often combines natural language processing with finite state machine generation to transform bug reports into actionable test models. Guizzo's publication record reveals a consistent trajectory of increasing impact in top-tier software engineering venues. His recent work demonstrates sophisticated integration of multi-objective evolutionary algorithms with practical software testing challenges, particularly in the areas of model inference from natural language bug reports and optimization of mutation testing processes. The evolution of his research shows progression from foundational work on design patterns in evolutionary algorithms toward more complex applications in automated software testing and repair. His achievements include: SSBSE 2021 Challenge Winner for innovative work on fitness functions in automated program repair Distinguished Artifact Award at ICSE 2021 recognizing reproducibility in genetic improvement research Microsoft Azure Research Award in 2017 supporting his work in cloud-based software engineering research Guizzo maintains active engagement with the research community through editorial roles for IET Software and Science of Computer Programming, membership on the SSBSE Steering Committee, and service on program committees for major conferences including ESEC/FSE, ASE, GECCO, and ICSE. His academic visits to University College London (3 months in 2017) and Queen Mary University (1 month in 2017) established lasting research collaborations with Dr. Jens Krinke, Dr. Federica Sarro, and Dr. John Drake that continue to produce high-impact publications. Through his interdisciplinary approach combining software engineering, evolutionary computation, and natural language processing, Guizzo has established himself as a significant contributor to the search-based software engineering community, with research that consistently addresses practical challenges while advancing theoretical foundations.

Dirk Pflügerمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

Toni Mattisمشاهده پروفایل

پژوهشگر

• Software modularity
• Software metrics
• Exploratory programming environments
• +۵ مورد دیگر

Toni Mattis is a Ph.D. researcher and teaching assistant at the Software Architecture Group of the Hasso Plattner Institute (University of Potsdam, Germany). His work integrates artificial intelligence into software engineering practices, focusing on live programming environments , code repositories mining , and test prioritization . His research interests center around: Software modularity and architectural knowledge reification Exploratory programming and live programming environments Machine learning applications on code repositories Test prioritization techniques using AI and lexical analysis Recent publications analyze: AI-generated dynamic context for program comprehension (2024) Parallel version control for exploratory programming (2025) Game-based contexts for example-driven development (2024) Task complexity modeling in software maintenance (2022-2023) Scientific recognition includes: 2018 3rd Prize ACM Student Research Competition 2017 2nd Prize ACM Student Research Competition German Academic Scholarship Foundation (2010-2014) Multiple national computer science contest awards (2009) As educator, he has designed and supervised: Master's seminars on AI for Programming (2024) Code repository mining courses (2017-2020) Software testing modules covering TDD, BDD, and mutation testing