LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
Journal of Educational and Behavioral Statistics25, 101–132 (2000)
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AutoSLO applies genetic programming inside a monitoring loop to evolve scaling policies that cut resource use in microservices while keeping SLO violations low and short-lived.
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Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software
LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
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Genetic Programming for Self-Adaptive Auto-Scaling of Microservices
AutoSLO applies genetic programming inside a monitoring loop to evolve scaling policies that cut resource use in microservices while keeping SLO violations low and short-lived.