Repov012kirigirirar Hot «CONFIRMED»

Modern software ecosystems increasingly rely on that automatically evolve in response to workload, security, and performance pressures. The Repov012Kirigirirar framework (hereafter R‑K‑Hot ) is a recent prototype that integrates dynamic code hot‑swapping , temperature‑aware load balancing , and self‑optimizing version control . In this paper we (i) formalize the notion of “repository temperature” as a quantitative indicator of mutational pressure and runtime stress, (ii) develop a stochastic model of R‑K‑Hot’s hot‑swap dynamics , and (iii) propose a set of temperature‑driven optimization policies that reduce mean‑time‑to‑failure (MTTF) by up to 37 % in simulated cloud‑native workloads. Experimental evaluation on a Kubernetes‑based testbed demonstrates that temperature‑aware scheduling outperforms baseline static policies while preserving functional correctness. Our results suggest that temperature‑centric management is a viable path toward resilient, self‑healing software supply chains.

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[ \mathbfs(t) = \bigl[ \underbracec(t) \textcommit rate,; \underbracep(t) \textpatch size,; \underbracev(t) \texttest‑coverage volatility,; \underbracee(t) \textruntime exception rate,; \underbracer(t)_\textresource consumption\bigr]^!\top ]

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