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Resources

Testing Guides & Best Practices

Explore comprehensive guides, tutorials, and best practices for automated testing, debugging, and quality assurance. Stay updated with the latest testing tools and techniques.

Taming Flaky Tests with Debug AI: Trace-Replay Architectures

Taming Flaky Tests with Debug AI: Trace-Replay Architectures

A practical, opinionated blueprint for building a deterministic trace capture and replay pipeline with time-travel debugging and event timelines to fix flaky tests reliably, even when LLMs struggle with non-determinism.

RAG for Debugging AI: Logs, Runbooks & Incidents as Context

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Showing 16-21 of 21 resources

RAG for Debugging AI: Logs, Runbooks & Incidents as Context

A practical blueprint for building retrieval-augmented debugging AI: ingest code, traces, runbooks, and postmortems; choose embeddings and indexes; ensure freshness, governance, and privacy to cut MTTR.

DebuggAI Team

18 min read

6/30/2026

Code-Debugging AI vs Race Conditions: Happens-Before Evidence

Code-Debugging AI vs Race Conditions: Happens-Before Evidence

LLMs miss concurrency bugs. Learn how to capture lock graphs, vector clocks, and reproducible traces so code debugging AI can pinpoint races, deadlocks, and heisenbugs with causal proof—wired into your tests, CI, and IDE.

RAD: Retrieval-Augmented Code Debugging That Reproduces Bugs

RAD: Retrieval-Augmented Code Debugging That Reproduces Bugs

How to build retrieval-augmented code debugging AI that uses logs, traces, failing test artifacts, and git history to reproduce bugs and ship verifiable fixes. Includes data contracts, CI hooks, privacy, and evaluation methodology.

Deterministic Replay for Code-Debugging AI

Deterministic Replay for Code-Debugging AI

How to snapshot inputs, env, network, and time to feed code-debugging AI deterministic sessions—enabling reproducible analysis, safe patches, and audit trails across CI/CD, microservices, and prod hotfixes.

Turn Your Debug AI into a Real Engineer with MCP

Turn Your Debug AI into a Real Engineer with MCP

A practical architecture for code debugging ai with Model Context Protocol: plug into test runners, sandboxes, logs, and VCS using least-privilege, audit trails, and deterministic repros.