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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.

Stop Guessing: Turn Your Code Debugging AI into a Real Debugger with GDB, LLDB, and eBPF

Stop Guessing: Turn Your Code Debugging AI into a Real Debugger with GDB, LLDB, and eBPF

A practical, opinionated blueprint for wiring LLMs to native debuggers, DWARF/PDB symbols, and record‑replay so your debugging AI sets breakpoints, inspects memory, traces syscalls, and reproduces flaky bugs deterministically—without leaking source.

Magic Ring Crochet Isn’t Forever: Reinforced Starts That Beat the Magic Circle (and Never Pop Open)

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Magic Ring Crochet Isn’t Forever: Reinforced Starts That Beat the Magic Circle (and Never Pop Open)

Tired of magic rings loosening? Learn why centers pop open and master reinforced starts—double magic ring, anchored tail, waistcoat start, felted lock—plus when to use each.

DebuggAI Team

17 min read

12/10/2025

From Stack Traces to System Traces: How Debug AI Should Use eBPF and OpenTelemetry to Diagnose Prod Incidents Safely

From Stack Traces to System Traces: How Debug AI Should Use eBPF and OpenTelemetry to Diagnose Prod Incidents Safely

A practical, opinionated blueprint for combining eBPF and OpenTelemetry with a debug AI to triage production incidents fast and safely—minimal capture, deterministic replay, and zero-SSH operations.

Beads Memory for AI Coding Agents: An Architecture that Automates PM in Developer Workflows

Beads Memory for AI Coding Agents: An Architecture that Automates PM in Developer Workflows

A deep dive into Beads: a lightweight, temporal-graph memory and issue tracker that lets AI coding agents plan, triage, and coordinate long-horizon work—outperforming vector-only memory and brittle prompt chaining.

Runtime-First Debug AI: Marrying Observability and RAG to Fix What Tests Miss

Runtime-First Debug AI: Marrying Observability and RAG to Fix What Tests Miss

A practical blueprint for feeding traces, logs, and core dumps into a debugging AI via observability-driven RAG, deterministically replaying failures, and auto-minimizing bug reports without leaking secrets or exploding costs.

From Stack Trace to Pull Request: Architecting an Observability‑Driven Code Debugging AI

From Stack Trace to Pull Request: Architecting an Observability‑Driven Code Debugging AI

A practical, observability‑first blueprint for wiring logs, traces, and snapshots into an automated pipeline that reproduces failures, generates minimal tests, proposes safe patches, verifies fixes, and opens pull requests without leaking prod data.

When Stack Traces Attack: Prompt-Injection Risks in Code Debugging AI—and How to Defend

When Stack Traces Attack: Prompt-Injection Risks in Code Debugging AI—and How to Defend

Your IDE’s debug AI can be hijacked by logs, comments, and test data. We map the threat model, demo exploits, and deliver a mitigation playbook: trace sanitization, tool sandboxing, least-privilege APIs, and safe prompting patterns.

Trace-RAG for Code Debugging AI: Grounding Fixes in Production Evidence

Trace-RAG for Code Debugging AI: Grounding Fixes in Production Evidence

A blueprint for wiring logs, spans, and heap snapshots into code debugging AI so fixes are reproducible and tied to real requests. Covers OpenTelemetry context, data retention, privacy, and how to evaluate grounded patches.

Differential Fuzzing Meets Code Debugging AI: Exposing Heisenbugs in Distributed Systems

Differential Fuzzing Meets Code Debugging AI: Exposing Heisenbugs in Distributed Systems

Blueprint for pairing property-based tests, shadow execution, and LLM reasoning to surface nondeterministic bugs in microservices—without flaky tests or production risk.

RAG for Stack Traces: Building a Debugging AI That Reproduces Bugs, Not Just Explains Them

RAG for Stack Traces: Building a Debugging AI That Reproduces Bugs, Not Just Explains Them

How to architect a debugging AI that replays failures: capture execution traces, build causal slices, use trace‑RAG and delta debugging in CI, and auto‑triage flaky tests—so fixes are reproducible, safe, and reviewable.

Stop Hallucinated Fixes: A RAG Blueprint for Code Debugging AI That Reads Your Build Graph

Stop Hallucinated Fixes: A RAG Blueprint for Code Debugging AI That Reads Your Build Graph

A practical, repo-and-build-aware RAG architecture for code debugging AI. Index symbols, ASTs, stack traces, logs, and build graphs; add query planning and caching; wire it into CI to ground suggestions and reduce noisy patches.

Deterministic Replay: The Missing Infrastructure for Code-Debugging AI

Deterministic Replay: The Missing Infrastructure for Code-Debugging AI

Most debug AIs fail on flaky bugs. Deterministic record/replay, eBPF traces, and time‑travel debugging can give AI a reproducible execution context to isolate root causes and produce reliable fixes.

Build a Flight Recorder for Code Debugging AI: Repro Traces, Env Snapshots, and One-Click Rollbacks

Build a Flight Recorder for Code Debugging AI: Repro Traces, Env Snapshots, and One-Click Rollbacks

How to instrument your AI code debug agents with full trace capture, prompts, diffs, artifacts, and environment snapshots—enabling deterministic repros, audits, and safe rollbacks inside fast CI/CD.

Deterministic Replay for Code Debugging AI: Making Bugs Reproducible, Fixes Verifiable

Deterministic Replay for Code Debugging AI: Making Bugs Reproducible, Fixes Verifiable

How to capture full execution traces so a debugging AI can reproduce failures, test patches, and ship verifiable PRs: hermetic builds, syscall recording, sandboxing, CI.

Prompt Injection Through Logs: The Hidden Attack Surface of Debug AI

Prompt Injection Through Logs: The Hidden Attack Surface of Debug AI

Attackers can hide instructions in logs and stack traces to hijack AI debug agents. Learn threat models, log scrubbing, sandboxed tools, and CI/CD guardrails to keep agents trustworthy.

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