GrasP: Graph-to-Sequence Learning for Automated Program Repair

Many deep learning models, for example, neural machine translation (NMT) models, have been developed for Automated Program Repair (APR). Due to the advantages of NMT model's strong generalization ability and less manual in-tervention, NMT-based methods perform well in APR. However, previous NMT-based APR approaches regard a code snippet as a sequence of tokens, which ignores the inherent structure of code. In this paper, we propose a novel end-to-end approach with Graph-to-Sequence learning, GrasP, to generate patches for buggy methods. To better represent the buggy method, we use a graph based on abstract syntax tree (AST) to represent the source code. In order to learn complex graph representation, we introduce the attention-based encoder-decoder model for graph-to-sequence learning. The empirical evaluation on the popular benchmark Defects4J shows that GrasP can generate compilable patches for 75 bugs, of which 34 patches are correct.