Marcelo D'Amorim

Marcelo D'Amorim is an Associate Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His research focuses on improving software reliability through advanced program analysis and systematic testing methodologies. With a Ph.D. from the University of Illinois Urbana-Champaign (2007), he has established himself as a leading researcher in software engineering and programming languages.

Dr. D'Amorim's educational background includes a Master's and Bachelor's degree from Universidade Federal de Pernambuco (2001 and 1996 respectively), providing him with a strong foundation in computer science before his doctoral studies in the United States.

His research interests center on Software Engineering and Programming Languages, with specific focus on improving software reliability through program analysis and systematic testing. He investigates practical methods to prevent, detect, and fix bugs in code, developing tools to automate software testing and debugging activities. His recent work has increasingly incorporated machine learning approaches, particularly large language models, to address longstanding challenges in software quality assurance. His research spans areas including software testing, bug detection, program analysis, and security analysis, with applications in various domains including deep learning libraries and cryptographic APIs.

Analysis of his recent publications reveals a clear trend toward leveraging artificial intelligence to solve traditional software engineering problems. His work increasingly focuses on LLM applications for test oracle generation, vulnerability repair, and code quality improvement, while maintaining strong foundations in traditional program analysis techniques. The research spans both theoretical foundations and practical tool development, with many of his publications including publicly available implementations.

Dr. D'Amorim has secured significant research funding, including an NSF grant for 'eSLIC: Enhanced Security Static Analysis for Detecting Insecure Configuration Scripts' (2020-2025, $199,978). This project aims to develop automated techniques to identify security weaknesses in configuration scripts to prevent large-scale security attacks and data breaches.

His academic service includes roles on program committees for major conferences including ASE'25 and ICSE'26. He teaches courses such as 'Software Testing and Reliability' at NC State University, connecting his research directly to classroom instruction. His research group appears to focus on practical software engineering tools with real-world applications, particularly in the areas of software testing, debugging, and security analysis.