Testing Guides & Best Practices

Heisenbugs break AI fixes. We outline a deterministic workflow where debug AI captures OpenTelemetry traces, synthesizes minimal repros, and generates tests before patching—cutting flakiness, rollbacks, and CI noise.

Your debug AI reads traces and logs—and attackers know it. Learn how adversarial log lines hijack code fixes, plus defenses: redaction, schema validation, sandboxed tools, and human-in-the-loop review.

Combine probabilistic LLM reasoning with deterministic symbolic execution to find and fix real bugs. Architecture, tools, integration patterns, pitfalls, and trust guidelines for building production-grade debugging AI.

A blueprint for capturing traces and I/O to deterministically replay production failures locally so a debug AI can localize faults and propose fixes. Covers record/replay techniques, test synthesis, data privacy, infra cost, and a sample toolchain.

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.

How to deploy code debugging AI in shadow mode: ingest logs, traces, and deterministic replay to triage incidents, pinpoint root causes, and propose safe fixes—with SLO gates, rollbacks, and audit trails.

A practical architecture for a code debugging AI: fuse OpenTelemetry traces, code graphs, and test replays into causal hypotheses; rank fixes; curb hallucinations with sandboxed repro and contracts. Patterns, pitfalls, and success metrics.

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.

Blueprint for integrating OpenTelemetry, logs, and service graphs into a code debugging AI for microservices—cutting false suggestions, speeding root-cause analysis, and enabling shadow patches with canary rollouts.

RAG alone won’t debug production. This article outlines a practical architecture for a code debugging AI that grounds itself in traces, failing tests, coverage, logs, and sandboxed reproductions to produce verifiable fixes with fewer hallucinations.

A practical, opinionated blueprint for building a retrieval-augmented code debugging AI that ingests incidents, logs, stack traces, commits, and tests; designs schemas and embeddings; ranks context; auto-generates repros; and integrates with CI, IDEs, and PRs with safety gates.

A practical blueprint for deterministic record/replay that feeds LLM debuggers: syscalls, network, heap snapshots, symbols, overhead vs fidelity, local vs cloud, privacy, and a production-ready pipeline.

Most bugs aren't hard—they're unreproducible. Learn how to design hermetic, data-provisioned sandboxes that let a code debugging AI capture, replay, and fix failures reliably across microservices, seeds, and time.