Web of Things (WoT) technology standardizes the integration of various IoT devices deployed in daily environments, promoting the capability of applications to automatically sense and regulate the physical environment.Meanwhile, the complex nature of such a ubiquitous software system, where heterogeneous applications, user activities, and environment states collectively influence device behaviors, poses risks of unexpected or even hazardous safety and security violations caused by improper device operations.Existing works on WoT violation identification primarily focus on the sole analysis of software applications, however, lacking consideration of the multi-source violations stemming from the human-cyberphysical ternary spaces, as well as the intricate interplay between environment and devices.Furthermore, the investigation into users' preferences for violation resolution remains unexplored.To address these limitations, we introduce EnvGuard, an environment-centric approach for customizing safety and security properties, identifying violations, and executing resolutions in the WoT environment.Our evaluation in two real-world WoT systems shows that Env-Guard outperforms previous state-of-the-art works, and confirms its usability, effectiveness, and runtime efficiency. CCS Concepts•

Continuous Integration and Deployment (CI/CD) pipelines are critical to modern software engineering, yet diagnosing and resolving their failures remains complex and labor-intensive. We present LogSage, the first end-to-end LLM-powered framework for root cause analysis (RCA) and automated remediation of CI/CD failures. LogSage employs a token-efficient log preprocessing pipeline to filter noise and extract critical errors, then performs structured diagnostic prompting for accurate RCA. For solution generation, it leverages retrieval-augmented generation (RAG) to reuse historical fixes and invokes automation fixes via LLM tool-calling.On a newly curated benchmark of 367 GitHub CI/CD failures, LogSage achieves over 98% precision, near-perfect recall, and an F1 improvement of more than 38% points in the RCA stage, compared with recent LLM-based baselines. In a yearlong industrial deployment at ByteDance, it processed over 1.07M executions, with end-to-end precision exceeding 80%. These results demonstrate that LogSage provides a scalable and practical solution for automating CI/CD failure management in real-world DevOps workflows.