inferencebench-code 0.0.2

Code-generation plugin for InferenceBench Suite (HumanEval-style execution-based scoring).

inferencebench-code

Code-generation plugin for the InferenceBench Suite.

HumanEval-style execution-based benchmarks: the plugin sends a function-signature prompt to the model, extracts the Python code from its response, executes it against bundled unit tests in a subprocess, and reports pass_at_1.

Suite ID: code.generation

Bundled benchmarks:

• code.generation.humaneval-mini — 5 stdlib-only Python tasks, pass_at_1 scoring with a 5-second per-task wall-clock timeout.

SAFETY WARNING — read before running

This plugin executes model-generated code. Every run prints a yellow banner reminding you of that. The execution layer is best-effort defence-in-depth, not a real sandbox:

• Each task's solution + tests are written to a temp file and invoked with python -I (isolated mode) under a subprocess.run(timeout=...) wall clock.
• A cheap substring pre-scan refuses any solution that imports subprocess, os.system, socket, urllib, multiprocessing, or ctypes.
• The bundled fixtures are stdlib-only, no I/O, no network.

This is deliberately not airtight. Phase 2 adds real isolation (firejail / nsjail / container-per-task). Until then: only run code-generation benchmarks against models you trust, on machines you can afford to throw away, and never against the bundled fixtures replaced with untrusted input.

Metrics

The envelope's metrics block includes:

Metric	Direction	Meaning
pass_at_1	higher is better	mean of per-task passed booleans
pass_at_1_p05/50/95	higher is better	bootstrap quantiles of per-sample scores
timeout_rate	lower is better	fraction of tasks that hit the wall clock
ttft_p50_ms	-	model time-to-first-token, median
total_p50_ms	-	model total request time, median
tokens_out_total	-	total generated tokens across the run
ok_rate	-	fraction of model calls that succeeded
n_samples	-	fixture row count