title: Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair
summary: The rapid advancement of bug-finding techniques has led to the discovery of
  more vulnerabilities than developers can reasonably fix, creating an urgent
  need for effective Automated Program Repair (APR) methods. However, the
  complexity of modern bugs often makes precise root cause analysis difficult and
  unreliable. To address this challenge, we propose crash-site repair to simplify
  the repair task while still mitigating the risk of exploitation. In addition,
  we introduce a template-guided patch generation approach that significantly
  reduces the token cost of Large Language Models (LLMs) while maintaining both
  efficiency and effectiveness.
    We implement our prototype system, WILLIAMT, and evaluate it against
  state-of-the-art APR tools. Our results show that, when combined with the
  top-performing agent CodeRover-S, WILLIAMT reduces token cost by 45.9% and
  increases the bug-fixing rate to 73.5% (+29.6%) on ARVO, a ground-truth open
  source software vulnerabilities benchmark. Furthermore, we demonstrate that
  WILLIAMT can function effectively even without access to frontier LLMs: even a
  local model running on a Mac M4 Mini achieves a reasonable repair rate. These
  findings highlight the broad applicability and scalability of WILLIAMT.
paper_authors._id: 682d7f7a176087390484a412
paper_authors.name: Han Zheng
paper_authors._id: 682d7f7a176087390484a413
paper_authors.name: Ilia Shumailov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:25:55.989Z
paper_authors._id: 6475c2794766357252e69e9f
paper_authors.avatarUrl: /avatars/757ed789423113369868e972f21ce559.svg
paper_authors.fullname: i
paper_authors.type: user
paper_authors.user: iliashum
paper_authors._id: 682d7f7a176087390484a414
paper_authors.name: Tianqi Fan
paper_authors._id: 682d7f7a176087390484a415
paper_authors.name: Aiden Hall
paper_authors._id: 682d7f7a176087390484a416
paper_authors.name: Mathias Payer
paper_title: Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair
paper_summary: The rapid advancement of bug-finding techniques has led to the discovery of
  more vulnerabilities than developers can reasonably fix, creating an urgent
  need for effective Automated Program Repair (APR) methods. However, the
  complexity of modern bugs often makes precise root cause analysis difficult and
  unreliable. To address this challenge, we propose crash-site repair to simplify
  the repair task while still mitigating the risk of exploitation. In addition,
  we introduce a template-guided patch generation approach that significantly
  reduces the token cost of Large Language Models (LLMs) while maintaining both
  efficiency and effectiveness.
    We implement our prototype system, WILLIAMT, and evaluate it against
  state-of-the-art APR tools. Our results show that, when combined with the
  top-performing agent CodeRover-S, WILLIAMT reduces token cost by 45.9% and
  increases the bug-fixing rate to 73.5% (+29.6%) on ARVO, a ground-truth open
  source software vulnerabilities benchmark. Furthermore, we demonstrate that
  WILLIAMT can function effectively even without access to frontier LLMs: even a
  local model running on a Mac M4 Mini achieves a reasonable repair rate. These
  findings highlight the broad applicability and scalability of WILLIAMT.
paper_ai_summary: Crash-site repair and template-guided patch generation reduce token costs and improve bug-fixing rates in automated program repair.
paper_ai_keywords: crash-site repair template-guided patch generation Large Language Models token cost Automated Program Repair