Learning to Represent Programs with Heterogeneous Graphs

by   Wenhan Wang, et al.

Program source code contains complex structure information, which can be represented in structured data forms like trees or graphs. To acquire the structural information in source code, most existing researches use abstract syntax trees (AST). A group of works add additional edges to ASTs to convert source code into graphs and use graph neural networks to learn representations for program graphs. Although these works provide additional control or data flow information to ASTs for downstream tasks, they neglect an important aspect of structure information in AST itself: the different types of nodes and edges. In ASTs, different nodes contain different kinds of information like variables or control flow, and the relation between a node and all its children can also be different. 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. We use the ASDL grammar of programming language to define the node and edge types of program graphs. Then we use heterogeneous graph neural networks to learn on these graphs. We evaluate our approach on two tasks: code comment generation and method naming. Both tasks require reasoning on the semantics of complete code snippets. Experiment results show that our approach outperforms baseline models, including homogeneous graph-based models, showing that leveraging the type information of nodes and edges in program graphs can help in learning program semantics.


page 1

page 2

page 3

page 4


Modular Tree Network for Source Code Representation Learning

Learning representation for source code is a foundation of many program ...

Learning to Represent Programs with Graphs

Learning tasks on source code (i.e., formal languages) have been conside...

Learning to Extend Program Graphs to Work-in-Progress Code

Source code spends most of its time in a broken or incomplete state duri...

Learning Graph Structure With A Finite-State Automaton Layer

Graph-based neural network models are producing strong results in a numb...

Towards Fully Declarative Program Analysis via Source Code Transformation

Advances in logic programming and increasing industrial uptake of Datalo...

An Evolver program for weighted Steiner trees

We present an algorithm to find near-optimal weighted Steiner minimal tr...

Exploring Representation of Horn Clauses using GNNs (technique report)

Learning program semantics from raw source code is challenging due to th...

Please sign up or login with your details

Forgot password? Click here to reset