Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python.
Existing methods often rely on LLM reflectors to propose harness updates, which can fix observed failures but also disrupt previously correct behavior.
Text-to-code tasks offer a natural way to mitigate this issue: we can execute generated outputs, allowing us to evaluate and verify the effects of proposed updates directly.
Building on this signal, we introduce \textsc{HERO} (\textbf{H}arnessing \textbf{E}vidence for \textbf{R}efinement and \textbf{O}ptimization), which combines offline and test-time harness optimization.
At the first stage, \method{} learns reusable prompt updates from execution-verified successes and failures and validates candidate revisions before adoption.
At test time, we treat the generated code as an instance-specific harness and refine it using execution feedback.
Across four SQL, Cypher, and Python benchmarks, \method{} achieves the best offline performance, improving pass@1 by \(7.6\) points over the initial prompt, while test-time refinement adds another \(2.0\) points and improves execution efficiency.