Source Code
Source code, the fundamental building block of software, is the subject of intense research focusing on improving its analysis, generation, and security. Current efforts leverage machine learning, particularly transformer-based models like BERT and GPT variants, and graph neural networks, to analyze code for vulnerabilities, predict defects, and even automatically generate code from natural language descriptions. These advancements have significant implications for software development, enhancing code quality, security, and developer productivity, while also raising new challenges related to code authorship attribution and the detection of AI-generated code.
Papers
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Sangdon Park, Xiang Cheng, Taesoo Kim
Automated Code Extraction from Discussion Board Text Dataset
Sina Mahdipour Saravani, Sadaf Ghaffari, Yanye Luther, James Folkestad, Marcia Moraes
Poison Attack and Defense on Deep Source Code Processing Models
Jia Li, Zhuo Li, Huangzhao Zhang, Ge Li, Zhi Jin, Xing Hu, Xin Xia
Follow-up Attention: An Empirical Study of Developer and Neural Model Code Exploration
Matteo Paltenghi, Rahul Pandita, Austin Z. Henley, Albert Ziegler
Code Librarian: A Software Package Recommendation System
Lili Tao, Alexandru-Petre Cazan, Senad Ibraimoski, Sean Moran
Leveraging Artificial Intelligence on Binary Code Comprehension
Yifan Zhang