FeedbackLLM uses line and branch coverage feedback agents in an iterative multi-agent process with a redundancy cache to generate test cases achieving higher coverage than baselines on standard C and Python benchmarks while scaling linearly in time.
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FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback
FeedbackLLM uses line and branch coverage feedback agents in an iterative multi-agent process with a redundancy cache to generate test cases achieving higher coverage than baselines on standard C and Python benchmarks while scaling linearly in time.