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Misaligned training pairs in code review datasets can undermine the effectiveness of LLMs, revealing that dataset cleaning alone won't solve the problem of generating actionable feedback.
Role-aware summaries can boost bug localization effectiveness by 40% while being 10.4 to 20.9 times more efficient than raw source code.