# Scanner Analyzer Microbench Harness

The bench harness exercises the language analyzers against representative filesystem layouts so that regressions are caught before they ship.

## Layout
- `StellaOps.Bench.ScannerAnalyzers/` – .NET 10 console harness that executes the real language analyzers (and fallback metadata walks for ecosystems that are still underway).
- `config.json` – Declarative list of scenarios the harness executes. Each scenario points at a directory in `samples/`.
- `baseline.csv` – Reference numbers captured on the 4 vCPU warm rig described in `docs/12_PERFORMANCE_WORKBOOK.md`. CI publishes fresh CSVs so perf trends stay visible.

## Current scenarios
- `node_monorepo_walk` → runs the Node analyzer across `samples/runtime/npm-monorepo`.
- `java_demo_archive` → runs the Java analyzer against `samples/runtime/java-demo/libs/demo.jar`.
- `python_site_packages_walk` → temporary metadata walk over `samples/runtime/python-venv` until the Python analyzer lands.

## Running locally

```bash
dotnet run \
  --project bench/Scanner.Analyzers/StellaOps.Bench.ScannerAnalyzers/StellaOps.Bench.ScannerAnalyzers.csproj \
  -- \
  --repo-root . \
  --out bench/Scanner.Analyzers/baseline.csv
```

The harness prints a table to stdout and writes the CSV (if `--out` is specified) with the following headers:

```
scenario,iterations,sample_count,mean_ms,p95_ms,max_ms
```

Use `--iterations` to override the default (5 passes per scenario) and `--threshold-ms` to customize the failure budget. Budgets default to 5 000 ms (or per-scenario overrides in `config.json`), aligned with the SBOM compose objective.

## Adding scenarios
1. Drop the fixture tree under `samples/<area>/...`.
2. Append a new scenario entry to `config.json` describing:
   - `id` – snake_case scenario name (also used in CSV).
   - `label` – human-friendly description shown in logs.
   - `root` – path to the directory that will be scanned.
   - For analyzer-backed scenarios, set `analyzers` to the list of language analyzer ids (for example, `["node"]`).
   - For temporary metadata walks (used until the analyzer ships), provide `parser` (`node` or `python`) and the `matcher` glob describing files to parse.
3. Re-run the harness (`dotnet run … --out baseline.csv`).
4. Commit both the fixture and updated baseline.