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School of Electronic and Computer Engineering
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CausalRepair fixes 313 bugs with a cost of just $0.029 per bug, outpacing state-of-the-art APR methods by leveraging minimal causal context.
Historical data can be dynamically adapted to achieve an impressive 1.732x speedup in compiler optimization, reshaping how we approach autotuning.
LLMs can fix 26% more bugs when given access to intermediate runtime states during program repair, proving that even the best models struggle to infer root causes from just failure symptoms.
Open-source LLMs can generate test suites rivaling GPT-4.2's quality, thanks to a new framework that treats test generation as a greedy optimization problem solvable via reinforcement learning.
