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
bnRep: A repository of Bayesian networks from the academic literature
Manuele Leonelli
CodeSCAN: ScreenCast ANalysis for Video Programming Tutorials
Alexander Naumann, Felix Hertlein, Jacqueline Höllig, Lucas Cazzonelli, Steffen Thoma
Defect Prediction with Content-based Features
Hung Viet Pham, Tung Thanh Nguyen