ML-Powered Root Cause Analysis and Automated Remediation for Java Microservices

Abstract

It is far from adequate to detect performance regressions in production Java microservices without proper attribution and resolution, especially in large, rapidly changing codebases without wide-ranging human involvement. This is challenging in modern continuous delivery environments, where multiple commits are bundled into a release and the root cause is inferred from heterogeneous streams of performance telemetry, version control, and incident history. In this article, let’s build an end-to-end system for code change analysis with multi-modal feature engineering, gradient-boosted tree classification, SHAP-based explanations, and large language model code generation. And design an ensemble XGBoost model that learns the non-linear mapping from code change to runtime impact. By using SHAP values in order to give theoretically principled, plain-language relevance explanations that ensure engineer trust that these models are calibrated. A LoRA fine-tuned GPT-4 model then writes production-ready code changes through an AI-orchestrated pull request workflow, with human approval and staged deployment verification remaining mandatory as gates. The automation becomes an accelerant to engineering judgment rather than a substitute for it. The system is continuously retrained based on feedback from engineers to accommodate codebase changes.