Learning to Represent Programs with Code Hierarchies
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Graph neural networks have been shown to produce impressive results for a wide range of software engineering tasks. Existing techniques, however, still have two issues: (1) long-term dependency and (2) different code components are treated as equals when they should not be. To address these issues, we propose a method for representing code as a hierarchy (Code Hierarchy), in which different code components are represented separately at various levels of granularity. Then, to process each level of representation, we design a novel network architecture, ECHELON, which combines the strengths of Heterogeneous Graph Transformer Networks and Tree-based Convolutional Neural Networks to learn Abstract Syntax Trees enriched with code dependency information. We also propose a novel pretraining objective called Missing Subtree Prediction to complement our Code Hierarchy. The evaluation results show that our method significantly outperforms other baselines in three tasks: any-code completion, code classification, and code clone detection.
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