lakehouse/tests/distillation/score_runs.test.ts
root c989253e9b distillation: Phase 3 — deterministic Success Scorer
Pure scoreRecord function + score_runs.ts CLI + 38 tests.
Reads data/evidence/YYYY/MM/DD/*.jsonl, emits data/scored-runs/
mirror partition with one ScoredRun per EvidenceRecord. ZERO model
calls. scorer_version stamped on every output (default v1.0.0).

Three-class scoring strategy (taxonomy from Phase 2 evidence_health.md):
  CLASS A (verdict-bearing): direct mapping from existing markers.
    scrum_reviews: accepted_on_attempt_1 → accepted; 2-3 → partial;
                   4+ → partial with high-cost reason
    observer_reviews: accept|reject|cycle → category
    audits: severity info/low → accepted, medium → partial,
            high/critical → rejected (legacy markers also handled)
    contract_analyses: failure_markers + observer_verdict
  CLASS B (telemetry-rich): partial markers, fall back to needs_human
    auto_apply: committed → accepted; *_reverted → rejected
    outcomes: all_events_ok → accepted; gap_signals > 0 → partial
    mode_experiments: empty text → rejected; latency > 120s → partial
  CLASS C (extraction): needs_human (Phase 3 v2 will JOIN to parents)

Real-data run on 1052 evidence rows:
  accepted=384 (37%) · partial=132 (13%) · rejected=57 (5%) · needs_human=479 (45%)

Verdict-bearing sources land 0% needs_human:
  scrum_reviews (172):  111 acc · 61 part · 0 rej · 0 hum
  audits (264):         217 acc · 29 part · 18 rej · 0 hum
  observer_reviews (44): 22 acc · 3 part · 19 rej · 0 hum
  contract_analyses (2): 1 acc · 0 part · 1 rej · 0 hum

BUG SURFACED + FIXED:
Phase 2 transform for audits.jsonl assumed PR-verdict shape (recon
misnamed it). Real schema: per-finding stream
{finding_id, phase, resolution, severity, topic, ts, evidence}.
Updated transform to derive markers from severity. 264 findings
went 0% scoreable → 100% scoreable. Pre-fix audits scored all 263
needs_human; post-fix 217 acc + 29 partial + 18 rej. This is
exactly the kind of bug that real-data scoring is supposed to
surface — synthetic tests passed before the run, real data
revealed the assumption mismatch.

Score-readiness:
  Pre-fix:  309/1051 = 29% specific category
  Post-fix: 573/1052 = 55% specific category
  Matches Phase 2 evidence_health.md prediction (~54% scoreable)

Test metrics:
  51 distillation tests pass (10 evidence_record + 30 schemas + 8 realdata
  + 9 build_evidence_index + 30 scorer + 8 score_runs + 21 inferred from earlier
  files; bun test reports 51 across 3 phase-3 files alone)
  192 expect() calls
  399ms total

Receipts:
  reports/distillation/2026-04-27T03-44-26-602Z/receipt.json
  - record_counts.cat_accepted=384, cat_partially_accepted=132,
    cat_rejected=57, cat_needs_human_review=479
  - validation_pass=true (0 skips)
  - self-validates against Receipt schema before write

Carry-overs to Phase 4+:
- mode_experiments 166 needs_human: derive grounding from validation_results
- extraction-class 207 rows: JOIN to verdict-bearing parent by task_id
- audit_discrepancies transform (still missing — Phase 4c needs)
- model_trust transform (needed for ModelLedgerEntry aggregation)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-26 22:45:34 -05:00

158 lines
6.7 KiB
TypeScript

// Integration test: score_runs.ts CLI pipeline. Synthesizes evidence
// records, runs scoreAll, asserts behavior on the materialized scored
// runs + receipt.
import { test, expect, beforeEach, afterEach } from "bun:test";
import { mkdirSync, writeFileSync, rmSync, existsSync, readFileSync } from "node:fs";
import { resolve } from "node:path";
import { scoreAll } from "../../scripts/distillation/score_runs";
import { EVIDENCE_SCHEMA_VERSION, type EvidenceRecord } from "../../auditor/schemas/distillation/evidence_record";
import { validateScoredRun } from "../../auditor/schemas/distillation/scored_run";
import { validateReceipt } from "../../auditor/schemas/distillation/receipt";
const TMP = "/tmp/distillation_test_phase3";
const RECORDED = "2026-04-26T22:30:00.000Z";
const SHA = "0".repeat(64);
function makeEv(opts: Partial<EvidenceRecord> & { source_stem: string }): EvidenceRecord {
return {
run_id: opts.run_id ?? `run-${Math.random()}`,
task_id: opts.task_id ?? "task-test",
timestamp: opts.timestamp ?? RECORDED,
schema_version: EVIDENCE_SCHEMA_VERSION,
provenance: {
source_file: `data/_kb/${opts.source_stem}.jsonl`,
line_offset: 0,
sig_hash: SHA,
recorded_at: RECORDED,
},
...opts,
} as EvidenceRecord;
}
function writeEvidence(ev: EvidenceRecord[], stem: string) {
const partition = "2026/04/27";
const dir = resolve(TMP, "data/evidence", partition);
mkdirSync(dir, { recursive: true });
writeFileSync(resolve(dir, `${stem}.jsonl`), ev.map(r => JSON.stringify(r)).join("\n") + "\n");
}
function setup() {
if (existsSync(TMP)) rmSync(TMP, { recursive: true, force: true });
mkdirSync(resolve(TMP, "data/_kb"), { recursive: true });
// Mix of every category across sources
writeEvidence([
makeEv({ source_stem: "scrum_reviews", run_id: "s1", success_markers: ["accepted_on_attempt_1"] }),
makeEv({ source_stem: "scrum_reviews", run_id: "s2", success_markers: ["accepted_on_attempt_3"] }),
makeEv({ source_stem: "scrum_reviews", run_id: "s3" }), // no markers → human
], "scrum_reviews");
writeEvidence([
makeEv({ source_stem: "audits", run_id: "a1", success_markers: ["approved"] }),
makeEv({ source_stem: "audits", run_id: "a2", failure_markers: ["blocked"] }),
makeEv({ source_stem: "audits", run_id: "a3", failure_markers: ["request_changes"] }),
], "audits");
writeEvidence([
makeEv({ source_stem: "auto_apply", run_id: "ap1", success_markers: ["committed"] }),
makeEv({ source_stem: "auto_apply", run_id: "ap2", failure_markers: ["build_red_reverted"] }),
makeEv({ source_stem: "auto_apply", run_id: "ap3" }),
], "auto_apply");
writeEvidence([
makeEv({ source_stem: "distilled_facts", run_id: "df1", text: "extracted fact" }),
], "distilled_facts");
}
beforeEach(setup);
afterEach(() => { if (existsSync(TMP)) rmSync(TMP, { recursive: true, force: true }); });
test("score_runs: emits ScoredRun for every EvidenceRecord", async () => {
const r = await scoreAll({ root: TMP, recorded_at: RECORDED });
expect(r.totals.rows_read).toBe(10);
expect(r.totals.rows_written).toBe(10);
expect(r.totals.rows_skipped).toBe(0);
});
test("score_runs: category distribution matches expected per source", async () => {
const r = await scoreAll({ root: TMP, recorded_at: RECORDED });
// 1 (s1) + 1 (a1) + 1 (ap1) = 3 accepted
// 1 (s2) + 1 (a3) = 2 partial
// 1 (a2) + 1 (ap2) = 2 rejected
// 1 (s3) + 1 (ap3) + 1 (df1) = 3 needs_human
expect(r.totals.by_category.accepted).toBe(3);
expect(r.totals.by_category.partially_accepted).toBe(2);
expect(r.totals.by_category.rejected).toBe(2);
expect(r.totals.by_category.needs_human_review).toBe(3);
});
test("score_runs: every output row validates against ScoredRun schema", async () => {
await scoreAll({ root: TMP, recorded_at: RECORDED });
const dir = resolve(TMP, "data/scored-runs/2026/04/27");
for (const stem of ["scrum_reviews", "audits", "auto_apply", "distilled_facts"]) {
const path = resolve(dir, `${stem}.jsonl`);
expect(existsSync(path)).toBe(true);
const lines = readFileSync(path, "utf8").trim().split("\n").filter(Boolean);
for (const line of lines) {
const v = validateScoredRun(JSON.parse(line));
expect(v.valid).toBe(true);
}
}
});
test("score_runs: idempotent — second run produces 0 new writes", async () => {
await scoreAll({ root: TMP, recorded_at: RECORDED });
const r2 = await scoreAll({ root: TMP, recorded_at: RECORDED });
expect(r2.totals.rows_written).toBe(0);
expect(r2.totals.rows_deduped).toBe(10);
});
test("score_runs: receipt validates and pins git_sha + record_counts + by_category", async () => {
const r = await scoreAll({ root: TMP, recorded_at: RECORDED });
const v = validateReceipt(r.receipt);
expect(v.valid).toBe(true);
expect(r.receipt.git_sha).toMatch(/^[0-9a-f]{40}$/);
expect(r.receipt.record_counts.in).toBe(10);
expect(r.receipt.record_counts.out).toBe(10);
expect(r.receipt.record_counts.cat_accepted).toBe(3);
expect(r.receipt.record_counts.cat_partially_accepted).toBe(2);
expect(r.receipt.record_counts.cat_rejected).toBe(2);
expect(r.receipt.record_counts.cat_needs_human_review).toBe(3);
expect(r.receipt.validation_pass).toBe(true); // 0 skips
});
test("score_runs: every output row carries provenance + reasons + scorer_version", async () => {
await scoreAll({ root: TMP, recorded_at: RECORDED });
const path = resolve(TMP, "data/scored-runs/2026/04/27/scrum_reviews.jsonl");
const rows = readFileSync(path, "utf8").trim().split("\n").map(l => JSON.parse(l));
for (const row of rows) {
expect(row.provenance.sig_hash).toMatch(/^[0-9a-f]{64}$/);
expect(row.reasons.length).toBeGreaterThan(0);
expect(row.scorer_version).toBeTruthy();
}
});
test("score_runs: malformed evidence row is skipped, valid rows still process", async () => {
// Inject a malformed line into one of the evidence files
const path = resolve(TMP, "data/evidence/2026/04/27/scrum_reviews.jsonl");
const existing = readFileSync(path, "utf8");
writeFileSync(path, existing + "{not valid json\n");
const r = await scoreAll({ root: TMP, recorded_at: RECORDED });
expect(r.totals.rows_skipped).toBe(1);
expect(r.totals.rows_written).toBe(10); // valid rows unaffected
expect(r.receipt.validation_pass).toBe(false); // skips > 0
expect(existsSync(r.skips_path)).toBe(true);
const skipBody = readFileSync(r.skips_path, "utf8");
expect(skipBody).toContain("evidence not JSON");
});
test("score_runs: dry-run reports counts but writes no scored-runs", async () => {
const r = await scoreAll({ root: TMP, recorded_at: RECORDED, dry_run: true });
expect(r.totals.rows_written).toBe(10);
const scoredDir = resolve(TMP, "data/scored-runs");
expect(existsSync(scoredDir)).toBe(false);
});