Testing Guides & Best Practices

How to build crash capture, time‑travel replay, and deterministic sandboxes so debug AI works from real signals—wiring logs, traces, and flaky tests into CI/CD without leaking secrets, boosting fix accuracy and time‑to‑merge.

Design an end-to-end CI pipeline that pairs deterministic replay with debug AI to isolate flaky roots, rank races, minimize repros, and propose fixes without breaking hermetic builds.

A practical blueprint for capturing deterministic bug reproductions—environment snapshots, time-travel traces, and CI hooks—to power and evaluate AI debugging, with privacy, cost, and telemetry trade‑offs and real‑world metrics.

A design for LLM breakpoints that bind prompts to stack traces, OpenTelemetry, and invariants, making code debugging AI observable, reproducible, and CI-ready with verifiable steps, seeds, and tests.

Turn logs, traces, and core dumps into deterministic, minimal repro tests with code debugging AI. Close the ops-dev loop, protect PII, and wire it into CI/CD without flaky artifacts or false positives.

A practical blueprint for feeding prod traces, logs, and crashes into debug AI: redaction pipelines, privacy budgets, RAG vs fine-tuning, synthetic repro cases, and compliance patterns that keep fixes fast and data safe.

A practical design for a code debugging AI that orchestrates eBPF probes, time-travel debugging, coverage-guided tests, and MCP tools to reproduce, isolate, and fix bugs—moving beyond chat and static linting.

A practical guide for engineering teams to build minimal, relevant context for code-debugging AI using static slicing, symbol graphs, and production log RAG—cut tokens, boost accuracy, and speed root-cause analysis.

Build a repo-aware pipeline that retrieves logs, traces, test artifacts, and git history so your code debugging AI can localize bugs, rank hypotheses, and propose verifiable fixes—fast, private, and CI-friendly.

How eBPF, record/replay, and redaction feed deterministic traces to code debugging AI—converting flaky prod bugs into repeatable fixes without log spam or risky prod writes. Covers architecture, pitfalls, and rollout steps.

Design a retrieval-augmented pipeline that feeds traces, logs, Git diffs, tests, and past incidents into a code debugging AI—cutting false fixes, speeding MTTR, and making AI suggestions reproducible in microservices.

Most LLM debuggers fail on deadlocks, races, and memory visibility. This article shows practical patterns to give code debugging AI a happens-before model via traces, schedule exploration, and static analysis—so it stops hallucinating fixes and finds real concurrency bugs.

How to pair a repo-time-indexed knowledge base with your CI so a code debugging AI can fetch exact code, logs, and configs from the failing build, avoid hallucinations, and propose reproducible fixes.