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.
A tool for test case scenarios generation using large language models
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PPO-LLM adaptively selects among eight prompting techniques using an 11-dimensional state vector to guide an LLM toward higher branch and line coverage than static baselines on 20 benchmark programs.
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.
citing papers explorer
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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.
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PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation
PPO-LLM adaptively selects among eight prompting techniques using an 11-dimensional state vector to guide an LLM toward higher branch and line coverage than static baselines on 20 benchmark programs.
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A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.