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