Opp repl fingerprint tests

Skill tabgab/opp_repl-skill/opp-repl-fingerprint-tests

Composable Anthropic-format Agent Skills for driving OMNeT++ simulations via opp_repl. Works in Claude, Windsurf, and any SKILL.md-aware agent.

Install

npx -y skills add tabgab/opp_repl-skill --skill opp-repl-fingerprint-tests

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What its author says it does

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Detect behavioral regressions using simulation event fingerprints (hash-based). run_fingerprint_tests compares a computed hash of selected state against a stored baseline in the project's fingerprint_store JSON. update_fingerprint_test_results seeds or refreshes that baseline. Load when you need trajectory-level regression detection stronger than smoke tests.

SKILL.md

4.1 KB, as published. Nobody here has run it

Fingerprint tests

Fingerprint tests hash a trajectory of per-event simulation state (event types, packet counts, etc.) and compare the result against a value stored in the project's fingerprint_store JSON file. A tiny behavioral change produces a completely different hash, catching regressions that pass smoke/statistical tests.

Upstream reference: https://github.com/omnetpp/opp_repl/blob/main/doc/fingerprint_tests.md

Baseline store

SimulationProject parameter fingerprint_store (default "fingerprint.json") points to a JSON file containing (config, run_number, sim_time_limit) -> fingerprint entries. INET convention: tests/fingerprint/store.json.

First-time seeding

update_fingerprint_test_results(simulation_project=inet_project,
                                sim_time_limit="1s")

Output includes INSERT lines for every new entry:

[04/42] Updating fingerprint . -c PureAlohaExperiment -r 3 for 1s INSERT 856a-c13d/tplx
...
Multiple update fingerprint results: INSERT, summary: 42 INSERT (unexpected) in 0:00:01.567

Re-running the update when nothing has changed gives KEEP (baseline preserved):

Multiple update fingerprint results: KEEP, summary: 7 KEEP in 0:00:00.218

Running the tests

r = run_fingerprint_tests(simulation_project=inet_project,
                          sim_time_limit="1s")

Output:

[02/42] Checking fingerprint . -c PureAlohaExperiment -r 1 for 1s PASS
...
Multiple fingerprint test results: PASS, summary: 42 PASS in 0:00:01.129

Re-run only failures, or narrow to a region after intentional changes:

r.get_fail_results().rerun()
update_fingerprint_test_results(simulation_project=inet_project,
                                working_directory_filter="examples/ethernet",
                                sim_time_limit="10s")

Command line

Typical CI workflow starting from an empty store:

# 1. No baseline yet -> every test SKIPs
opp_run_fingerprint_tests --load inet.opp -p inet -t 1s

# 2. Seed
opp_update_fingerprint_test_results --load inet.opp -p inet -t 1s

# 3. Tests PASS until behavior changes
opp_run_fingerprint_tests --load inet.opp -p inet -t 1s

Debugging a FAIL

A FAIL means the live simulation trajectory no longer matches the stored hash. Two useful follow-ups:

Pull the stdout / fingerprint trajectory from the failing TaskResult (see opp-repl-tasks-and-results):

failed = r.get_fail_results().results[0]
ft = failed.simulation_task_result.get_fingerprint_trajectory()
# compare to a known-good run

Use compare_simulations() or compare_simulations_between_commits() (see opp-repl-comparing-simulations) to locate the first divergent event.

Pitfalls

Any change to simulation time limits or random seeds CHANGES the fingerprint. Keep sim_time_limit stable across runs or regenerate the baseline.
Fingerprint entries are keyed by (config, run, time limit). Running the tests with a different sim_time_limit than was stored yields SKIP (no baseline for this key).
Commit the updated fingerprint_store JSON alongside intentional behavioral changes — otherwise CI will keep failing.
Fingerprint tests are deterministic only if the underlying simulation is deterministic. Non-deterministic features (e.g. real-time scheduler, certain exponential() RNG setups without fixed seeds) will produce PASS/FAIL flapping.