Accelerating Redundancy-Based Program Repair via Code Representation Learning and Adaptive Patch Filtering

Automated program repair (APR) has attracted extensive attention and many APR techniques have been proposed recently, in which redundancy-based techniques have achieved great success. However, they still suffer from the efficiency issue mainly caused by the inaccuracy of measuring code similarity, which may produce meaningless patches that hinder the generation and validation of correct patches. To solve this issue, we propose a novel method AccPR, which leverages code representation to measure code similarity and employs adaptive patch filtering to accelerate redundancy-based APR. We have implemented a prototype of AccPR and integrated it with a SOTA APR tool, SimFix, where the average improvement of efficiency is 47.85%, indicating AccPR is promising.