Ryan Rong
Ryan Rong
Home Institution
The Peddie School
Year Participated
2022
Year in School
High School
REU Faculty Mentor
Reyhaneh Jabbarvand
Research Area Interest
Artificial Intelligence
Biography & Research Abstract
Abstract:
To use machine learning and specifically deep learning for software analysis tasks that require detecting, localizing, and repairing software bugs, the first step is to have a (1) large and (2) high-quality training dataset of the buggy and non-buggy versions of the code. There are some bug datasets such as Defects4J and BugSwarm that involve real-world bugs collected from open-source projects. However, these datasets are relatively small, i.e., each contains around 800 unique bugs. State-of-the-art relies on mutation testing and specifically, higher-order mutants to inject artificial bugs into the code. However, there is always a debate about whether artificial bugs are representative of real bugs or not. In this project, we aim to use generative models to learn how to generate bugs that mimic real-world bugs. Such techniques can help with the generation of many bugs to use for training machine learning for code analysis tasks.
Bio:
I am a sophomore (10th grade) student at Peddie School. I am very interested in artificial intelligence and machine learning. I took Andrew Ng's machine learning course and implemented my own spam email classifier. I am familiar with machine learning frameworks such as Pytorch and TensorFlow, and implemented a hand sign recognition model. I am proficient in Java, C++, and Python, and passed the USA Computing Olympiad Silver Level. I also worked on web development and cybersecurity, and won a CyberStart America Silver Badge.