AI 에이전트를 활용한 분산 시스템 테스트

Distributed Systems Testing Skills Two skills for AI coding agents that design and run claim-driven tests for distributed and stateful systems. Together they produce a structured Markdown test plan and a findings report with 9-state verdicts and an explicit SUT / harness / checker / environment blame classification. A reviewer reads the two artifacts and decides whether to ship; nothing else has to be re-run. Works with Claude Code, Codex, Copilot CLI, Cursor, Gemini, or any agent that reads Markdown and runs shell. The skills are plain SKILL.md files. The agent executes them; the plan and findings report are the output. One skill designs the plan. The other runs it. A plan starts from the product's claims, generates hypotheses tied to those claims, and writes scenarios named after the claim each tries to falsify. For consistency-critical scenarios, each scenario also binds an abstract model ( register | queue | log | lock | lease | ledger | … ) to an operation-history schema, a named checker, and a nemesis with observable landing evidence. The plan ends with a coverage adequacy argument and a conservative confidence statement. Why The default for testing distributed and stateful systems — write a few integration tests and call it done — finds a small fraction of the bugs that actually break these systems in production: partial network partitions, non-deterministic concurrency, crash-recovery, upgrade/rollback, idempotency under replay, timing-sensitive ordering. These skills enforce an opinionated workflow that pulls from the field's hard-won knowledge: Claim-driven, not test-driven. Start from what the product promises. Every scenario falsifies one claim under one fault. A test named after its claim is harder to weaken than one named after its setup. Coverage adequacy is a deliverable. The plan ends with an argument that the chosen scenarios are enough to ship, plus an honest list of what stays unverified. Reuse the SUT's own toolbox. The execute skill discovers existing tests, runbooks, and fault-injection scaffolding before inventing anything new. Model + history + checker, not just chaos. For safety, durability, idempotency, isolation, ordering, or membership claims, every scenario declares an abstract model, an operation-history schema, a named checker (linearizability, serializability, session-consistency, no-lost-ack, exactly-once, …), and how it treats ambiguous outcomes (timeouts, unknown commits, retries). Chaos plus a model and a checker, not chaos alone. No silent passes. Every PASS cites oracle execution evidence and the signal proving the fault actually fired. Verdicts come from a 9-state set, so "the chaos script ran cleanly" can't be read as "the claim survived the fault." Every FAIL carries a SUT / harness / checker / environment blame tag so reproducers reach the right queue. What you get End-to-end, the two skills produce: testing-plans/<slug>.md ← plan with §0–§9 (see below) test-sessions/<UTC>/ ├── session-log.md ← timeline + toolbox + env probe ├── logs/ ← per-scenario stdout/stderr ├── metrics/ ← metric snapshots ├── artifacts/ ← ephemeral harnesses, dumps └── findings/ ├── <scenario>.md ← per-scenario verdict (written as run proceeds) └── report.md ← summary + adequacy + confidence delta The plan structure (a reviewer can read this and decide whether to ship without re-running the tests): 0. Architectural summary — system as it actually exists 1. Scope 1b. Claims under test — the spine 1c. Missing claims discovered — docs ↔ code drift 2. SUT model 3. Existing test inventory — what's already covered 4. Failure-mode hypotheses — tied to claim IDs 5. Coverage matrix — claim × hypothesis 6. Technique selection — from the catalog 6b. Environment requirements 7. Scenarios — each named after the claim, with Target test file + Skeleton 7.M Model / history / — mandatory when the scenario falsifies checker discipline a claim in {safety, durability, idempotency, isolation, ordering, membership}: model under test, operation-history schema, named checker, nemesis + landing evidence, ambiguous-outcome handling, reduction plan (SUT/harness/checker/env blame) 7b. Coverage adequacy argument — why these tests are enough 7c. Residual uncertainty — what stays unverified, and why ok 7d. Confidence statement — the reviewer's verdict 8. What this plan does NOT cover 9. Open questions / followups Example §7.M block (excerpt from a plan) ### Scenario S3: linearizable_append_under_partition - Falsifies if it FAILs: C1 (every acknowledged append is durable and linearisable), C5 (leader election completes within 5s) - Workload: 8 clients, 70% append / 30% read, 5min, key-skew zipf - Faults: asymmetric partition isolating current leader at T+60s for 30s - Oracle: linearizability via Porcupine over per-key histories §7.M (model / history / checker discipline) - Model under test: log - Operation history: default 11-field schema (op id, process id, invoke/complete ts, op type, key, input, output, error, timeout marker, node seen, fault epoch). Recorded in-process + server- side audit. - Checker: linearizability (Porcupine) per-key, then no-lost-ack against final state - Nemesis + landing: asymmetric-partition (iptables drop one direction). Landing evidence = iptables drop counter goes 0 → 14,712 over the 30s window AND raft log emits "leader-lost; starting election" within 2s of injection. - Ambiguous outcomes: timeouts → timeout_marker=true, complete_ts =null, treated as could-have-succeeded; retries are separate ops sharing input - Reduction plan: if FAIL, bisect fault window + fix seed, then classify SUT / harness / checker / environment per references/test-case-reduction.md Example findings-report row ID Verdict Nemesis landing evidence Reduction class S3 PASS-hardening iptables ctr 0→14,712; raft re-election at T+1.8s n/a S4 FAIL-reproducible partition landed; Elle: G2-item anomaly on key K17 SUT S7 INCONCLUSIVE-fault-not-proven iptables rule installed but counter stayed 0 — wrong chain harness S9 PARTIAL-model landing ok; checker covered per-key, not cross-key n/a (The full findings template carries Oracle, Oracle execution evidence, artifact links, an adequacy-vs-plan section, and a confidence delta — see skills/executing-distributed-system-tests/assets/findings-report-template.md .) Install (one line, any agent) Paste this at any AI coding agent (Claude Code, Codex, Copilot CLI, Cursor, Gemini, or anything else that reads Markdown and runs shell): Read https://raw.githubusercontent.com/shenli/distributed-system-testing/main/INSTALL.md and follow the instructions to install and configure distributed-testing-skills for this agent. The agent fetches INSTALL.md , clones the repo to ~/.local/share/distributed-testing-skills/ , and wires the skills in (symlinks under ~/.claude/skills/ for Claude Code, a pointer block in ~/AGENTS.md for other agents). After that, ask any agent on the machine to "design a test plan for this system" or "execute the plan at X" and it'll follow the SKILL.md workflow. Update Paste the same one-liner again. INSTALL.md is idempotent: if the install path exists, it does git pull --ff-only ; if not, it does git clone . Symlinks always point at the cloned content so they pick up the new version automatically. The ~/AGENTS.md pointer block uses HTML markers and is replaced cleanly on each run — no duplication. If you have local edits to the cloned skills, git pull --ff-only will fail; the agent will stop and ask before discarding them. Manual install (if you'd rather see what's happening) git clone https://github.com/shenli/distributed-system-testing.git \ ~ /.local/share/distributed-testing-skills # Claude Code: symlink under ~/.claude/skills/ mkdir -p ~ /.claude/skills ln -snf ~ /.local/share/distributed-testing-skills/skills/designing-distributed-system-tests \ ~ /.claude/skills/designing-distributed-system-tests ln -snf ~ /.local/share/distributed-testing-skills/skills/executing-distributed-system-tests \ ~ /.claude/skills/exec