TSAPR: A Tree Search Framework For Automated Program Repair
Haichuan Hu, Ye Shang, Weifeng Sun, Quanjun Zhang

TL;DR
TSAPR introduces a tree search framework for automated program repair that leverages Monte Carlo Tree Search to guide patch exploration, significantly improving repair success rates across multiple benchmarks.
Contribution
It presents TSAPR, a novel evaluate-and-improve framework integrating MCTS for more effective and efficient automated program repair across diverse defect types.
Findings
Successfully repaired 201 out of 835 bugs in Defects4J
Fixed 27 vulnerabilities in VUL4J
Resolved 164 out of 300 issues in SWE-Bench-Lite
Abstract
With the rapid advancement of Large Language Models (LLMs), traditional Automated Program Repair (APR) techniques have undergone significant transformation. Training-free approaches, such as zero-shot and few-shot prompting, are increasingly favored over fine-tuning-based methods, leveraging the strong code understanding and generation capabilities of LLMs to improve repair effectiveness. However, most existing LLM-based APR systems still follow a trial-and-error paradigm, which faces two fundamental challenges: (1) limited patch quality due to myopic, local exploration; and (2) inefficient search processes caused by redundant or unguided patch generation. To address these limitations, we propose TSAPR, a Tree Search-based APR framework designed for diverse types of software defects. Unlike conventional approaches, TSAPR adopts an evaluate-and-improve paradigm that systematically guides…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Scientific Computing and Data Management · Parallel Computing and Optimization Techniques
