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Found 6 papers, 3 papers with code
Self-Edit: Fault-Aware Code Editor for Code Generation
Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task.
Implant Global and Local Hierarchy Information to Sequence based Code Representation Models
Furthermore, we propose the Hierarchy Transformer (HiT), a simple but effective sequence model to incorporate the complete hierarchical embeddings of source code into a Transformer model.
CodeEditor: Learning to Edit Source Code with Pre-trained Models
Pre-trained models are first pre-trained with pre-training tasks and fine-tuned with the code editing task.
Learning Program Representations with a Tree-Structured Transformer
Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks.
What does Transformer learn about source code?
In the field of source code processing, the transformer-based representation models have shown great powerfulness and have achieved state-of-the-art (SOTA) performance in many tasks.
Learning to Represent Programs with Heterogeneous Graphs
To address the information of node and edge types, we bring the idea of heterogeneous graphs to learning on source code and present a new formula of building heterogeneous program graphs from ASTs with additional type information for nodes and edges.
