Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair
Authors: name: Han Zheng, name: Ilia Shumailov, name: Tianqi Fan, name: Aiden Hall, name: Mathias Payer

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.