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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.

Attachable Debug AI: Orchestrating gdb/lldb via MCP for Deterministic Bug Repro and Fixes

Attachable Debug AI: Orchestrating gdb/lldb via MCP for Deterministic Bug Repro and Fixes

How to build a code debugging AI that attaches to live processes, drives breakpoints, inspects memory, and proposes patches using the Model Context Protocol (MCP)—while keeping source and logs local. Covers architecture, tool APIs, IDE wiring, determinism, and safety trade-offs.

Benchmarks Are Broken: A Real-Repo Framework for Evaluating Code Debugging AI

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Benchmarks Are Broken: A Real-Repo Framework for Evaluating Code Debugging AI

Industry benchmarks mislead. This article proposes a CI-grade, real-repo evaluation harness for code debugging AI—with hermetic builds, OpenTelemetry traces, fix-rate and regression metrics, and flaky-test detection.

DebuggAI Team

17 min read

10/26/2025

Trace-Aware Debug AI: OpenTelemetry + eBPF for Production-Only Bugs

Trace-Aware Debug AI: OpenTelemetry + eBPF for Production-Only Bugs

Design and implement an AI-assisted debugging system that fuses OpenTelemetry traces with eBPF kernel events to pinpoint regressions and race conditions in live production systems—without PII, traffic pauses, or flaky repro steps.

Deterministic Replay or Bust: Repro Pipelines that Help Code Debugging AI Fix Production Bugs

Deterministic Replay or Bust: Repro Pipelines that Help Code Debugging AI Fix Production Bugs

Most bugs vanish on dev machines. Learn how to capture inputs, env, and side effects to build deterministic replays so a code debugging AI can reproduce, triage, and patch real prod issues without leaking secrets.

From Flaky CI to Failing Test: How debug ai Can Auto-Generate Minimal Reproducers

From Flaky CI to Failing Test: How debug ai Can Auto-Generate Minimal Reproducers

A blueprint for turning CI logs, traces, and git history into minimal failing tests your debug ai can iterate on, covering RAG prompting, sandboxing, and determinism without exposing secrets.

Beyond robots.txt: An Actions Manifest for Agentic Browsers

Beyond robots.txt: An Actions Manifest for Agentic Browsers

AI-driven browsing breaks UX assumptions. Define an Actions Manifest—intents, preconditions, quotas, and audit receipts—so agents can book, buy, and query safely without CAPTCHAs, brittle scraping, or bot-detection wars.

Should a Debug AI Commit Code? Guardrails for Autonomous Fixes in CI/CD

Should a Debug AI Commit Code? Guardrails for Autonomous Fixes in CI/CD

How to let a debug AI propose and commit fixes safely: repro from traces, tests-as-contracts, human approvals, sandboxing, and instant rollback.

Browser Agent Security Risks: CDP Automation Is Leaking Cookies, OAuth Tokens, and Internal App Data

Browser Agent Security Risks: CDP Automation Is Leaking Cookies, OAuth Tokens, and Internal App Data

AI copilots and headless agents that use Chrome DevTools Protocol (CDP) and WebDriver can exfiltrate sessions, bypass MFA, and pivot into intranets. This article explains attack chains and concrete defenses: ephemeral profiles, device‑bound sessions, CDP/WebDriver gating, and enterprise policies.

Grounded Debugging: How to Build a RAG Pipeline for Your Code Debugging AI

Grounded Debugging: How to Build a RAG Pipeline for Your Code Debugging AI

Build a RAG stack that feeds logs, traces, crash dumps, and failing tests into a code debugging AI—covering schemas, embeddings, privacy, evals, and CI/CD integration to reduce hallucinated fixes and speed root cause analysis.

Bisection Bots: Code Debugging AI That Reproduces, Bisects, and Auto-Patches Regressions in CI

Bisection Bots: Code Debugging AI That Reproduces, Bisects, and Auto-Patches Regressions in CI

A deep dive into building a verifiable code debugging AI pipeline—log mining to minimal repro, delta-bisect, sandboxed fixes, and guardrails. Metrics, rollback strategies, and how to ship AI-driven patches safely.

Trust, But Verify: Guardrailing Code Debugging AI to Prevent Silent Regressions

Trust, But Verify: Guardrailing Code Debugging AI to Prevent Silent Regressions

Unchecked AI-generated bug fixes can pass tests yet still regress behavior. Learn a practical blueprint for provenance, sandboxing, invariant checks, differential testing, and trace audits that make AI-driven debugging verifiable and safe.

When debugging AI writes the patch: Should we let agents auto-fix bugs in CI/CD?

When debugging AI writes the patch: Should we let agents auto-fix bugs in CI/CD?

Autonomous bug-fixing is here. Learn how to integrate debugging AI into CI/CD, assess risks, set guardrails, and roll out a safe pilot with measurable controls.

Who Debugs the Debugger? Auditing and Safeguarding Code Debugging AI in CI/CD

Who Debugs the Debugger? Auditing and Safeguarding Code Debugging AI in CI/CD

A practical guide to validate, sandbox, and monitor code-debugging AI using reproducible traces, patch verification, data governance, and rollback guardrails so AI fixes help rather than harm production.

Telemetry-RAG: Architecting a Safe Debugging AI That Reproduces Production Bugs and Ships Minimal Patches

Telemetry-RAG: Architecting a Safe Debugging AI That Reproduces Production Bugs and Ships Minimal Patches

A practical, opinionated blueprint for building a debugging AI that uses OpenTelemetry logs, traces, and execution snapshots to deterministically reproduce production failures in secure sandboxes, propose minimal patches with tests, and keep secrets and PII safe end to end.

RAG for Code Debugging AI: Turning Logs and Diffs into Instant Fixes

RAG for Code Debugging AI: Turning Logs and Diffs into Instant Fixes

How to build a private RAG stack for code debugging AI using stack traces, test failures, and PR history—covering data pipelines, retrieval schemas, evals, and guardrails to ship reliable, org-aware fixes.

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