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Code completion has become an essential component of integrated developmentenvironments. Contemporary code completion methods rely on the abstract syntaxtree (AST) to generate syntactically correct code. However, they cannot fullycapture the sequential and repetitive patterns of writing code and thestructural information of the AST. To alleviate these problems, we propose anew code completion approach named CCAG, which models the flattened sequence ofa partial AST as an AST graph. CCAG uses our proposed AST Graph Attention Blockto capture different dependencies in the AST graph for representation learningin code completion. The sub-tasks of code completion are optimized viamulti-task learning in CCAG, and the task balance is automatically achievedusing uncertainty without the need to tune task weights. The experimentalresults show that CCAG has superior performance than state-of-the-artapproaches and it is able to provide intelligent code completion.

Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs