Testing Guides & Best Practices

Build a privacy-safe RAG that turns logs, traces, and crash dumps into context for code debugging AI. We cover schema design, temporal retrieval, PII scrubbing, version pinning to commits, and how to wire it into on-call workflows.

Flaky tests poison AI debuggers. This article proposes a practical deterministic record/replay stack—covering syscalls, network, time, randomness, and scheduling—that empowers a code debugging AI to reliably bisect, minimize, and patch failures without exfiltrating code to the cloud.

A production-safe blueprint for debug AI: eBPF probes, record/replay snapshots, least‑privilege sandboxes, and redaction pipelines that deliver rich runtime context to LLMs without latency hits or PII exposure.

A practical, opinionated guide to integrating rr and Java Flight Recorder with an AI debugging pipeline that auto-minimizes failing runs, traces causality, and drafts safe patches. Covers architecture, data budgets, PII safety, and CI rollout strategy with real-world pitfalls.

How to confine a debugging AI with the Model Context Protocol (MCP): strict tool contracts, scoped tokens, full audit logs, and ephemeral sandboxes that prevent secret leaks and prod access.

Your logs, traces, and metrics double as prompts. Learn instrumentation patterns that make code debugging AI accurate, fast, and safe—structured logs, trace IDs, redaction, and feedback loops.

Concurrency bugs humble LLMs. This blueprint pairs code debugging ai with deterministic replay, schedule fuzzing, and time-travel debugging to reproduce races, pinpoint root cause, and validate fixes safely.

Most debugging AI fails without reproducible bugs. Learn how to capture minimal failing traces, time-travel logs, and environment snapshots to enable safe, deterministic fixes.

Build a time‑travel debugging pipeline where LLMs reason over deterministic execution traces (rr, JFR, eBPF). Learn capture strategies, privacy, prompt grounding, CI gating, and how replay slashes flaky, prod‑only bug hunts.

A practical, opinionated blueprint for granting a debugging AI controlled, auditable access to Kubernetes logs, exec, and metrics via MCP/tooling—complete with RBAC, session recording, rate limiting, and break-glass policies—so it can diagnose incidents without risking outages.

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.

Stop guessing. We show how a debugging AI pairs record/replay (rr, Undo) with causal analysis to auto-bisect, run counterfactual tests, and propose minimal, safe fixes, turning logs and traces into reproducible, actionable repairs.

How to build debug AI assistants without leaking secrets: threat models, structured redaction, on‑device inference, DLP‑aware tracing, consented sampling, and auditable data flows.