kube-score
Static code analysis for Kubernetes definitions with opinionated security and reliability checks.
Automate code reviews and optimize application performance with machine learning-driven insights.
Amazon CodeGuru is an enterprise-grade developer tool suite that leverages machine learning to automate code reviews and application performance profiling. By 2026, CodeGuru has become deeply integrated into the Amazon Q Developer ecosystem, serving as the specialized engine for deep semantic analysis and runtime behavior optimization. It consists of two primary components: CodeGuru Reviewer, which utilizes program analysis and machine learning to detect critical issues, security vulnerabilities, and hard-to-find bugs during development; and CodeGuru Profiler, which provides interactive visualizations of application performance in production. The tool's architecture is built on models trained on millions of lines of open-source and internal Amazon code, allowing it to identify non-obvious defects such as resource leaks, race conditions, and inefficient CPU utilization. As the market shifts toward 'Shift-Left' security and 'FinOps' for cloud resources, CodeGuru's ability to map code efficiency directly to infrastructure costs positions it as a vital utility for organizations managing high-scale distributed systems. It supports major languages like Java and Python, with expanded support for JavaScript, TypeScript, and C# via the Amazon Q integration layer.
Uses deep learning and automated reasoning to identify security vulnerabilities like hardcoded credentials and injection attacks.
Static code analysis for Kubernetes definitions with opinionated security and reliability checks.
Automated security auditing and remediation for high-integrity Kubernetes clusters.
Automated Kubernetes security compliance auditing against CIS Benchmarks.
The AI Software Engineer for automated code reviews and proactive quality assurance.
Verified feedback from the global deployment network.
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Visual representation of the most 'expensive' code paths in terms of CPU and latency.
Provides specific code snippets to fix identified bugs, which can be applied directly to pull requests.
Analyzes code for improper handling of untrusted data in log files to prevent log forging.
Detects unclosed file handles, database connections, and network sockets.
Can be configured as a blocking check in pipelines to prevent insecure code from reaching production.
Specialized checks for data science libraries like NumPy and Scikit-learn.
An application is consuming excessive CPU, leading to high EC2 or Lambda costs.
Registry Updated:2/7/2026
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Manual security reviews are too slow for daily deployments.
Intermittent race conditions causing data corruption in high-concurrency environments.