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

Tao Zhang1, Yu Zhu1

  • 1Shanxi Police College, Taiyuan, Shanxi, China.

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|March 18, 2026
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Summary
This summary is machine-generated.

MuTemAPR, an automated program repair (APR) approach, effectively addresses complex multilocation faults by iteratively generating patches. This method improves upon existing techniques by simulating real-world scenarios and considering fault interdependencies for more accurate code repair.

Keywords:
automated program repairfix templatesmultilocation patchneural machine translation

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Area of Science:

  • Software Engineering
  • Automated Program Repair
  • Artificial Intelligence

Background:

  • Existing automated program repair (APR) methods struggle with multilocation faults, often generating single-position patches.
  • Current multistep repair techniques lack realistic data augmentation and overlook crucial interdependencies between faulty code segments.
  • This leads to patches learned from incorrect contextual patterns, limiting repair effectiveness in complex scenarios.

Purpose of the Study:

  • To introduce MuTemAPR, a novel APR approach designed for iterative generation of multilocation patches.
  • To enhance data augmentation by simulating real-world fault scenarios and incorporating variable-type mapping.
  • To improve repair accuracy by considering fault interdependencies and enforcing syntactic/semantic rules.

Main Methods:

  • Developed a template-based data augmentation framework using 35 mutation templates to create realistic multilocation faulty code.
  • Implemented a reinforced faulty context training method with progressive annotation for incremental learning in multifault code.
  • Integrated a semantic constraint mechanism to enforce code correctness through differential analysis during training.

Main Results:

  • MuTemAPR successfully repairs multilocation faults, outperforming state-of-the-art methods on the Defects4j benchmark.
  • The approach fixed five additional bugs compared to existing methods on Defects4j v1.2 and v2.0.
  • Experimental results validate the effectiveness of the template-based augmentation and reinforced training strategies.

Conclusions:

  • MuTemAPR offers a significant advancement in automated program repair for complex multilocation faults.
  • The proposed innovations in data augmentation and training methods enhance the accuracy and applicability of APR.
  • This work provides a more robust solution for real-world software defect fixing.