MuTemAPR: Enhance Multilocation Patches with Template-Based Neural Program Repair

Automated program repair (APR) has been studied extensively in recent years. Existing approaches mainly generate single-position patches that fail to address multilocation faults effectively. While existing multistep repair approaches can iteratively generate patches for each fault position sequentially, their data augmentation methodologies lack rationality and deviate from real-world scenarios. Furthermore, they overlook the interdependencies between faulty statements, leading to patches learned from erroneous contextual patterns. In this article, we propose MuTemAPR, an APR approach that iteratively generates multilocation patches. MuTemAPR incorporates templates with neural machine translation. Specifically, our method introduces three key innovations. First, we design a template-based data augmentation framework that transforms single-line faulty code into multilocation faulty code through 35 mutation templates. It simulates a real-world environment by establishing variable-type mapping tables for more accurate repair augmentation. Second, we propose a reinforced faulty context training method that employs progressive annotation to incrementally learn repair processes from top to bottom in multifault code. Third, we implement a semantic constraint mechanism during training that enforces syntactic and semantic rules through differential analysis between templates, input code, and generated patches. We evaluate MuTemAPR on the widely used Defects4j benchmark. Experimental results demonstrate that our approach can effectively repair multilocation faults, successfully fixing five additional bugs compared with state-of-the-art methods on Defects4j v1.2 and v2.0.