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The results reveal a fundamental risk of LLM-generated regression tests: without manual validation, they may encode faulty behavior as expected behavior, allowing bugs to persist across software revisions and largely evade developer-maintained test suites.
This work introduces a search-based approach that identifies and evolves a set of natural language transformation rules with strong downstream effects on coding performance, and proposes DUALFIX, a staged repair pipeline that combines the evolved transformation rules with execution-feedback repair, addressing both spec...
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