Continuous Observability-Driven Development: Integrating Telemetry, AI-Based Debugging, and DevSecOps Pipelines
Keywords:
observability, telemetry, AI-based debugging, DevSecOpsAbstract
To be able to see what's going on, big distributed and cloud-native systems need proactive engineering. This research gives us SDLC logs, metrics, and traces that are spread out. Telemetry, feedback, DevSecOps pipelines, and AI-based debugging all help make systems safer, more dependable, and quicker. CODD employs machine learning models to find problems, figure out what caused them, and automatically look into the root cause to minimize MTTR and make systems more resilient and code better. This article discusses about architectural principles for integrating telemetry, observability-as-code, and AI-assisted reasoning for safe CI/CD problem handling and compliance checking. Researchers investigate into feedback automation loops that let development, operations, and security teams adjust how a system functions while it's running. Development observability makes operations intelligence, software maturity, and governance efficiency all better.
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