Source Code Model

Source code models are deep learning systems trained on vast amounts of code to understand and generate software. Current research focuses on improving these models' ability to generalize across different programming tasks and code styles, often employing transformer architectures and graph neural networks to capture both syntactic and semantic information within code. A key challenge is enhancing the models' contextual understanding, particularly by incorporating broader project-level context beyond immediate code snippets, and addressing issues of out-of-distribution generalization. These advancements hold significant promise for automating software development tasks, improving code quality, and accelerating software engineering workflows.