ApplySharifجستجوی استاد راهنمای دانشگاهی

Software refactoring Professors and Supervisors | ApplySharif

جستجوی استاد راهنمای دانشگاهی

برای مسیر پژوهشی‌تان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آن‌ها ارتباط بگیرید.

نمایش ۴۵۱-۴۶۴ از ۴۶۴ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Eunjong Choiمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Eunjong Choi is an Associate Professor at the Faculty of Information and Human Sciences, Kyoto Institute of Technology in Japan. She joined Kyoto Institute of Technology as a tenure-track assistant professor in 2019 and was promoted to associate professor in March 2024. Prior to this position, she served as an assistant professor at Nara Institute of Science and Technology (April 2016-March 2019) and at Osaka School of International Public Policy, Osaka University (April 2015-March 2016). Kyoto Institute of Technology (2019-Present) Nara Institute of Science and Technology (2016-2019) Osaka University (2015-2016) Dr. Choi's research focuses on software engineering with particular emphasis on software maintenance and evolution, program comprehension, software analytics, reused code management and detection, refactoring support, and test code generation. Her work bridges theoretical software engineering concepts with practical applications in industrial settings. She has investigated topics such as token-level SZZ algorithms, deep learning-based clone detectors, and modernization of large-scale industrial systems. Dr. Choi has held significant leadership roles in major software engineering conferences including serving as General Chair for ICPC 2020 and IWESEP 2017, Publicity Co-Chair for ICSE 2020, and Local Arrangement Co-Chair for SANER 2016. She has been actively involved in the IEEE Kansai Section Woman in Engineering Affinity Group, serving as Secretary (2016-2017) and Vice-Chair (2018-2021). Her teaching portfolio includes Software Mining and Analysis, Software Metrics, and Software Engineering courses at Kyoto Institute of Technology; Software Design and related courses at Nara Institute of Science and Technology; and Basic Information Literacy at Osaka University. Dr. Choi earned her Ph.D. in Information Science and Technology from Osaka University in 2015 and maintains an active research profile in the software engineering community.

پروفایل رسمی دانشگاه

Fuyuki Ishikawaمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Fuyuki Ishikawa is an Associate Professor at the Information Systems Architecture Science Research Division of the National Institute of Informatics (NII) in Tokyo, Japan, where he also serves as Director of the GRACE Center. Additionally, he holds positions as Associate Professor at Sokendai (The Graduate University for Advanced Studies) and Visiting Associate Professor at The University of Electro-Communications. His research program, which he describes as 'Trustworthy & Smart Software Engineering,' focuses on dependability in Cyber-Physical Systems and Machine Learning Systems through techniques of formal methods and automated test generation. He investigates verification, reasoning, optimization, automated test generation, and self-adaptation by utilizing various models for requirements, specifications, and designs. His group promotes international and industry-academia collaborations with members from different organizations. Ishikawa's recent work has increasingly concentrated on quantum program testing, autonomous driving systems verification, and quality assurance frameworks for AI systems. His publications demonstrate significant contributions to software engineering, particularly in applying formal methods to emerging domains like quantum computing and machine learning. His research often bridges theoretical foundations with practical applications in safety-critical contexts, particularly for autonomous systems. Awards for Science and Technology (Research Category) from the Minister of Education, Culture, Sports, Science and Technology (April 2024) IPSJ/IEEE Computer Society Young Researcher Award for 'Research on Intelligence-driven Engineering of Dependable Smart Systems' (March 2020) Multiple Best Paper Awards at conferences including ICFEM 2020, SEKE 2020, ICECCS 2019, and ISSRE 2019 Industry Track Best Artifact Award at ISSTA 2015 Ishikawa actively supervises research projects and interns through the ERATO-MMSD Project and MIRAI-eAI Project. He has served on numerous program committees for major software engineering conferences including ASE, ICSE, and ICST, and has organized workshops on machine learning systems engineering (SIG-MLSE) and quality assurance for AI systems (QA4AI). His laboratory provides two main research directions: generative AI for trustworthy software engineering and testing/trust exploration for AI systems, with specific focus on autonomous driving and deep learning applications.

پروفایل رسمی دانشگاه

Ding Liمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)

پروفایل رسمی دانشگاه

Apostolos Ampatzoglouمشاهده پروفایل

دانشیار

University of MacedoniaGreece

Apostolos Ampatzoglou serves as Associate Professor in the Department of Applied Informatics at the University of Macedonia, where he leads research in software engineering with particular emphasis on technical debt management and software quality. Previously, he held an Assistant Professor position at the University of Groningen (2013-2016) and maintains active collaborations across European research initiatives. His academic credentials include a PhD in Software Engineering from Aristotle University of Thessaloniki (2013), MSc in Computer Systems from the University of Macedonia (2005), and BSc in Informatics from the Technological Education Institute of Thessaloniki (2003). Dr. Ampatzoglou's research program centers on technical debt quantification, reverse engineering, and software maintainability. He pioneered financial models for technical debt management and developed metrics for assessing ripple effects in software systems. His work bridges theoretical foundations with industrial applications through projects like SDK4ED and SmartCLIDE, focusing on energy-efficient development and cloud service engineering. Current investigations explore machine learning applications for vulnerability prediction and technical debt prioritization. His extensive publication record reveals strong trends in applying AI to software quality assurance, with recent work emphasizing transformer models for vulnerability detection and explainable AI for technical debt identification. He actively investigates energy-aware development practices and service-oriented architecture patterns in cloud environments. Notable recognitions include: Top-7 reviewer for Journal of Systems and Software (2022) Best paper awards at IGSCC'21, ICSR'18, and EASE'17 18th most active Consolidated Researcher in Software Engineering (2013-2020) 3rd most active Early Stage Researcher (2010-2017) Multiple personal research grants from National Scholarships Association He has successfully supervised PhD candidates including Dr. Areti Ampatzoglou (Best PhD Thesis Award, University of Groningen) and Dr. Elvira-Maria Arvanitou (VERSEN Institute award). His research is funded through major European projects such as SDK4ED, SmartCLIDE, and SKILLAB, with recent grants exceeding €2M in total funding. Current work focuses on FAIR data implementation and skills gap analysis in European labor markets. As leader of the Software Engineering Lab at the University of Macedonia, he directs a 15+ member team working on technical debt management tools, cloud development environments, and XR applications for warehouse management. The lab maintains strong industry partnerships with technology clusters in Thessaloniki and participates in the Eclipse Foundation ecosystem.

پروفایل رسمی دانشگاه

Xin Xiaمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

پروفایل رسمی دانشگاه

Hui Liuمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.

پروفایل رسمی دانشگاه

Mehdi Bagherzadehمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Mehdi Bagherzadeh is an Assistant Professor in the Department of Computer Science and Engineering at Oakland University. His professional profile shows consistent involvement in major software engineering conferences including ASE, SPLASH, and ICSE, where he has served in various leadership roles such as Workshops Co-Chair for SPLASH (2021-2023) and Late Breaking Results Co-Chair for ASE (2022). Dr. Bagherzadeh's research focuses on the correct construction of concurrent and big data software, situated at the intersection of Software Engineering and Programming Languages. His work particularly addresses challenges in deep learning program optimization, actor concurrency models, and automated refactoring techniques. His research trajectory shows a clear evolution from foundational work on concurrent programming models (Panini, Capsule) to current applications in deep learning systems. Analysis of his publication history reveals a strong focus on practical software engineering challenges in concurrent systems, with recent work concentrating on the migration and optimization of deep learning programs. His research combines empirical studies with formal methods, addressing both theoretical foundations and practical implementation concerns in modern software systems. Through his committee service across multiple top-tier conferences including ASE, SPLASH, ICSE, and ESEC/FSE, Dr. Bagherzadeh has established himself as an active contributor to the software engineering research community. His service roles have ranged from program committee membership to organizational leadership positions.

پروفایل رسمی دانشگاه

Andreas Zellerمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Andreas Zeller serves as Professor for Software Engineering at Saarland University and faculty at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. His dual appointments position him at the intersection of academic research and practical cybersecurity applications, contributing significantly to both institutions' research profiles. Professor Zeller's research spans multiple dimensions of software quality assurance, with particular expertise in automated debugging techniques, mining software repositories for insights, specification mining, and security testing methodologies. His work consistently bridges theoretical foundations with practical implementation, resulting in tools and frameworks adopted widely in both research and industry contexts. Analysis of his recent publications reveals an evolutionary trajectory from foundational debugging work toward increasingly sophisticated grammar-based testing approaches, with notable integration of machine learning techniques in recent years. His research demonstrates consistent focus on improving software reliability through automated analysis, with growing emphasis on security applications including XML injection testing, GNSS module security, and binary file format vulnerabilities. Recipient of two ERC Advanced Grants (including the S3 project) ACM Fellow ACM SIGSOFT Outstanding Research Award Professor Zeller has successfully secured substantial research funding through competitive mechanisms including ERC grants, enabling his team to pursue ambitious research agendas. His leadership extends to mentoring through his roles as doctoral symposium co-chair and active participation in new faculty development initiatives. He maintains strong engagement with the research community through numerous program committee memberships and conference organization roles. At CISPA Helmholtz Center for Information Security, Zeller contributes to the center's mission through research focused on software security testing and analysis. His work on grammar-based testing and fuzzing directly addresses critical security challenges in modern software systems, with practical applications for improving software resilience against attacks.

پروفایل رسمی دانشگاه

Raffi Khatchadourianمشاهده پروفایل

دانشیار

Hunter College, CUNYUnited States

Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College and a member of the Doctoral Faculty of The Graduate School and University Center's Ph.D. Program in Computer Science at the City University of New York (CUNY). He leads the PONDER Lab @ CUNY and is a member of the CUNY Institute of Computer Simulation, Stochastic Modeling, and Optimization (CoSSMO). His research lies at the intersection of software engineering, programming languages, and reliable machine learning systems. He investigates how program analysis, automated refactoring, and type theory can ease the burden of correctly, efficiently, and securely evolving large and complex software. His work spans several key areas including automated refactoring of Java programs, empirical studies of software development practices, migration of imperative Deep Learning programs to graph execution, and the application of software engineering methodology to improve statistical programs and artificial intelligence. His research has been externally supported by the National Science Foundation (NSF), the Japan Society for the Promotion of Science (JSPS), Amazon Web Services (AWS), and the Verizon Foundation. Dr. Khatchadourian's recent publications demonstrate a strong focus on improving Deep Learning systems through automated refactoring techniques, analyzing technical debt in machine learning systems, and addressing concurrency challenges in modern programming languages. His work frequently appears in top-tier conferences including ICSE, ASE, ESEC/FSE, and FASE. His scientific achievements have been recognized with numerous awards including the EAPLS Distinguished Paper Award at FASE '25, EAPLS Best Paper Award at FASE '20, and a Distinguished Paper Award at IEEE SCAM '18. He has also received fellowships from JSPS and NSF EAPSI, along with the Eleanor Quinlan Memorial Award for Excellence in Teaching. As an advisor, Dr. Khatchadourian has mentored numerous PhD, Master's, and undergraduate students, including Tatiana Castro Vélez who recently accepted a tenure-track Assistant Professor position at University of Puerto Rico. He has served on multiple program committees for major conferences including ICSE, ASE, ECOOP, and GPCE, and has organized events such as the New York Seminar on Programming Languages and Software Engineering (NYPLSE). He also mentors students through NYU GSTEM and ACM SIGPLAN-M programs. Dr. Khatchadourian maintains active research collaborations across institutions and leads the PONDER Lab @ CUNY, which focuses on programming, optimization, and novel development environments for reliable software. The lab provides opportunities for undergraduate, master's, and doctoral students interested in programming languages and software engineering research.

پروفایل رسمی دانشگاه

Marcos Kalinowskiمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Professor Marcos Kalinowski is a faculty member in the Department of Informatics at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. He serves as Professor of Software Engineering with an active research program focused on the intersection of software engineering and artificial intelligence. His research interests span: Software Engineering Machine Learning in Software Engineering Empirical Software Engineering Requirements Engineering for AI/ML Systems Trustworthy AI Systems Software Engineering Education Professor Kalinowski's recent work has concentrated on Large Language Models (LLMs) applications in software engineering processes, emphasizing empirical validation and practical relevance. His publications reveal a strong commitment to understanding how AI technologies can be effectively integrated into software development while maintaining quality standards and addressing human factors. He leads the international "Naming the Pain in Requirements Engineering" (NaPiRE) initiative, which investigates requirements engineering challenges across global organizations. His scientific contributions include: Framework development for trustworthy AI systems Empirical studies on LLMs in software engineering research Investigations of human factors in software development Analysis of machine learning code quality and technical debt Requirements engineering methodologies for AI-enabled systems Professor Kalinowski holds leadership positions in major software engineering conferences including General Co-Chair for ICSE 2026 and Program Co-Chair for ESEM 2021. His work appears consistently in top-tier venues such as ICSE, ESEC/FSE, EASE, and SANER, demonstrating significant recognition within the software engineering research community.

پروفایل رسمی دانشگاه

Lu Xiaoمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Lu Xiao is an Assistant Professor in the School of Systems and Enterprises at Stevens Institute of Technology, where she conducts research in software engineering with a focus on software architecture, software economics, cost estimation, and software ecosystems. Dr. Xiao completed her PhD in Computer Science at Drexel University in 2016 under the supervision of Dr. Yuanfang Cai. Her doctoral research focused on the relationship between software architecture and quality attributes. Her research spans several key areas in software engineering with emphasis on empirical methods. She investigates how software architecture influences quality attributes, studies software economics and cost estimation techniques, and examines the dynamics of software ecosystems. Her work frequently analyzes real-world software projects, particularly those in the Apache Software Foundation, providing insights into software maintenance patterns, testing practices, and performance issues. Dr. Xiao has developed practical tools like SAIN for software architecture infrastructure and eFish'nSea for performance education. Analysis of Dr. Xiao's publication record reveals consistent contributions to empirical software engineering, particularly in software architecture analysis, testing methodologies, and performance issues. Her research often centers on Apache projects, demonstrating methodological rigor in studying real-world development practices. She has made significant advances in understanding test refactoring, mocking frameworks, and the identification of performance bottlenecks through linguistic analysis of issue reports. Dr. Xiao has actively contributed to the software engineering research community through service on program committees for major conferences including ASE, ICSE, ESEC/FSE, and ICSA. Her work on program committees spans multiple tracks including Research Papers, Student Research Competition, and specialized workshops. As an academic advisor, Dr. Xiao guides graduate students in research on software architecture analysis, testing practices, and performance optimization. Her research methodology combines empirical analysis of large software repositories with tool development and validation, ensuring practical relevance of her findings. Dr. Xiao leads research efforts focused on understanding the relationship between software architecture and quality attributes. Her current work on bots in pull requests represents cutting-edge research into automation in open source development processes, continuing her tradition of investigating real-world software engineering phenomena.

پروفایل رسمی دانشگاه

Masud Rahmanمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Software Engineering
Artificial Intelligence for Software Engineering (AI4SE)
Software Maintenance and Evolution
+۱۱ مورد دیگر

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

پروفایل رسمی دانشگاه

Chaiyong Ragkhitwetsagulمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Chaiyong Ragkhitwetsagul is an Assistant Professor at the Faculty of ICT, Mahidol University, Thailand, and co-founder of the SERU research group. His research focuses on software engineering with emphasis on maintenance, evolution, and program comprehension. Education: PhD in Software Engineering, University College London (UCL) MS in MSIT-Very Large Information Systems, Carnegie Mellon University BE in Computer Engineering, Kasetsart University His work investigates code proficiency metrics for Python and JavaScript to improve maintenance practices, employs search-based techniques for multi-objective clone configuration, and pioneers autorepairability as a novel software quality dimension. Recent publications demonstrate strong empirical focus on developer productivity and code quality assessment. 2024 publications reveal a cohesive trend toward practical tool development (e.g., jscefr) and empirical validation of software quality characteristics, bridging theoretical research with real-world engineering challenges. Active across major software engineering venues (ASE, ICSE, ICSME), he contributes to the community through program committees while leading the SERU research group's mission to advance software engineering science at Mahidol University.

پروفایل رسمی دانشگاه

Leopoldo Teixeiraمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

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.

پروفایل رسمی دانشگاه