Title: GRAPHIX: A Pre-trained Graph Edit Model for Automated Program Repair. Abstract: We present GRAPHIX, a pre-trained graph edit model for automatically detecting and fixing bugs and code quality issues in Java programs. Unlike sequence-to-sequence models, GRAPHIX leverages the abstract syntax structure of code and represents the code using a multi-head graph encoder. Along with an autoregressive tree decoder, the model learns to perform graph edit actions for automated program repair. We devise a novel pre-training strategy for GRAPHIX, namely deleted sub-tree reconstruction, to enrich the model with implicit knowledge of program structures from unlabeled source code. The pre-training objective is made consistent with the bug fixing task to facilitate the downstream learning. We evaluate GRAPHIX on the Patches in The Wild Java benchmark, using both abstract and concrete code. Experimental results show that GRAPHIX significantly outperforms a wide range of baselines including CodeBERT and BART and is as competitive as other state-of-the-art pre-trained Transformer models despite using one order of magnitude fewer parameters. Further analysis demonstrates strong inductive biases of GRAPHIX in learning meaningful structural and semantic code patterns, both in abstract and concrete source code.