Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs
This work addresses code completion for developers by improving accuracy and intelligence in integrated development environments, though it appears incremental as it builds on existing AST-based methods.
The authors tackled the problem of code completion by modeling flattened abstract syntax trees as graphs to better capture sequential patterns and structural information, achieving superior performance compared to state-of-the-art methods.
Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture the sequential and repetitive patterns of writing code and the structural information of the AST. To alleviate these problems, we propose a new code completion approach named CCAG, which models the flattened sequence of a partial AST as an AST graph. CCAG uses our proposed AST Graph Attention Block to capture different dependencies in the AST graph for representation learning in code completion. The sub-tasks of code completion are optimized via multi-task learning in CCAG, and the task balance is automatically achieved using uncertainty without the need to tune task weights. The experimental results show that CCAG has superior performance than state-of-the-art approaches and it is able to provide intelligent code completion.