FLARE extracts specifications from multi-agent LLM code and applies coverage-guided fuzzing to achieve 96.9% inter-agent and 91.1% intra-agent coverage while uncovering 56 new failures across 16 applications.
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Organizational policies constrain agency in AI-mediated software engineering more than individual preferences, with seniors using detailed delegation and pre-AI instincts while juniors oscillate between over-reliance and avoidance.
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FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systems
FLARE extracts specifications from multi-agent LLM code and applies coverage-guided fuzzing to achieve 96.9% inter-agent and 91.1% intra-agent coverage while uncovering 56 new failures across 16 applications.
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From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering
Organizational policies constrain agency in AI-mediated software engineering more than individual preferences, with seniors using detailed delegation and pre-AI instincts while juniors oscillate between over-reliance and avoidance.