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ARGUS reliably identifies root causes in Kubernetes incidents but struggles to gain trust for its prescriptive recommendations, revealing a critical gap in automated incident response systems.
Labeling code as LLM-generated significantly alters developers' attention and review strategies, revealing a critical gap between intention and behavior in code reviews.
Ensemble voting strategies can significantly enhance performance anomaly detection, achieving an 11% boost in F1-score while reducing false positives.
Developers overwhelmingly trust and directly apply LLM-generated code refactoring suggestions, but when they don't, the changes are surprisingly drastic and predictable.
