Alapfogalmak
Code pre-trained models and large language models have varying capabilities in learning and representing the syntax and semantics of programming languages, with syntax being more effectively captured than semantics.
Kivonat
The study extensively analyzes seven code models, including four prominent pre-trained models (CodeBERT, GraphCodeBERT, CodeT5, and UnixCoder) and three large language models (StarCoder, CodeLlama, and CodeT5+), to investigate their abilities in learning code syntax and semantics.
Key highlights:
- Syntax analysis:
- Code models effectively capture syntactic relationships between token pairs, with this property being more observable in the shallow hidden layers.
- Code models are proficient at identifying the syntax roles of individual tokens, with this property being more observable in the deep hidden layers.
- Code pre-trained models show superior performance than large language models for syntax tagging.
- Semantics analysis:
- Code models provide varying effectiveness in predicting semantic relationships, such as control and data dependencies.
- Current models highlight areas for improvement in capturing complex semantic structures, particularly in control flow graphs.
- Large language models do not generally outperform pre-trained models significantly in semantic tasks.
- Attention analysis indicates that different attention heads are specialized for distinct roles in encoding code semantics.
The findings suggest that code models still need to be improved to effectively learn and represent code syntax and semantics, with a focus on integrating these properties more deeply into the model representations.
Statisztikák
The number of edges in the Abstract Syntax Tree (AST) is not necessarily related to the closeness of code syntax.
The distance mapping from high-dimensional vector space to low-dimensional AST space may not be accurate.
Idézetek
"Significantly, recent works [14, 28, 75] indicate that code models cannot give reasonable results if token replacement or insertion tricks are used."
"Regarding the code models, we should consider deeply their ability to learn the basic characteristics of the code, 'What kind of code knowledge can these code models learn?'."