Automated program repair (APR) is a technology that identifies and repairs bugs automatically. However, repairing multi-chunk bugs remains a long-standing and challenging problem because an APR technique must consider dependencies and then reduce the large patch space. In addition, little is known about how to combine individual candidate patches even though multi-chunk bugs require combinations. Therefore, we propose a novel APR technique called multi-code repair (MCRepair), which applies a buggy block, patch optimization, and CodeBERT to target multi-chunk bugs. A buggy block is a novel method that binds buggy chunks into a multi-buggy chunk and preprocesses the chunk with its buggy contexts for patch space reduction and dependency problems. Patch optimization is a novel strategy that effectively combines the generated candidate patches with patch space reduction. In addition, CodeBERT, a BERT for source code datasets, is fine-tuned to address the lack of datasets and out-of-vocabulary problems. We conducted several experiments to evaluate our approach on six project modules of Defects4J. In the experiments using Defects4J, MCRepair repaired 65 bugs, including 21 multi-chunk bugs. Moreover, it fixed 18 unique bugs, including eight multi-chunk bugs, and improved 40 to 250 percent performance than the baselines.

翻译：自动程序修复（APR）是一种能够自动识别并修复缺陷的技术。然而，修复多块缺陷始终是一个长期存在的挑战性问题，因为APR技术必须考虑依赖关系并缩减庞大的补丁空间。此外，尽管多块缺陷需要组合多个补丁，但目前鲜有研究探讨如何有效组合单个候选补丁。为此，我们提出一种新型APR技术——多代码修复（MCRepair），通过应用错误块、补丁优化及CodeBERT来针对多块缺陷。错误块是一种创新方法，它将多个缺陷块绑定为多缺陷块，并利用其缺陷上下文对块进行预处理，从而缩减补丁空间并解决依赖问题。补丁优化是一种新型策略，可在缩减补丁空间的同时有效组合生成的候选补丁。此外，面向源代码数据集的BERT模型CodeBERT经过微调，以解决数据集不足和词汇外问题。我们在Defects4J的六个项目模块上开展了多项实验评估该方法。实验结果表明，MCRepair共修复65个缺陷，其中包含21个多块缺陷；尤其值得关注的是，它成功修复了18个独特缺陷（含8个多块缺陷），性能相比基线方法提升40%至250%。