Open_Ultra_SAST/.kiro/specs/engine-change-scoped-analysis/brief.md

Engine change-scoped analysis (G2 workstream)

Status: proposed / in progress (opened 2026-10-11). Owner: engine. Tracks a concrete lever for the engine-detection gap found while driving the G0 baseline. This is G2 ("improve what moves the unseen-repo numbers"), gated on the pool metric; it is NOT part of the G0 record-07 baseline (that runs on the quick-rule tier).

Problem (measured, not assumed)

On real unseen vulnerability changes the engine produces no finding — but the diagnosis (live, 300 s long-deadline replays) shows it is usually not a detection-capability gap:

• Clean Python injection change (aws/bedrock-agentcore-sdk-python, install_packages, introducing, 3-file diff): wall=303 s, hit the deadline. The ranker found candidates (static_rank=1.0) but selected: [] and every candidate deferred with reason: deadline_exhausted, analysis_not_started=deadline_exhausted, change_context_incomplete. Zero analyses ran. And the ranked candidates were repo-wide and unrelated to the push (auth.py access_control/config_secrets), not the changed install_packages. The engine is trying to re-audit the whole repo and runs out of budget before analyzing the change.
• Noisy last_touch config_secrets change (jenkinsci/metrics-plugin, 14-file diff): finished in 206 s but comparison_unknown + capability_unavailable + eligibility_provenance_mismatch + contributor-scan ... projection_unavailable. Multi-causal; correspondence was one part (fixed separately, commit c39fbb8), capability registry is Task 8.3.

Hypothesis

A pre-push hook should vet the code this push changed, not re-scan the whole repository. Seeding/scoping the engine's analysis to the changed declarations and their dataflow neighborhood (the change_sites are already in the replay spec) should let analysis actually run within budget instead of being deferred — which is where detection is being lost, not in Joern's capability.

Anchors (confirm in increment 1)

• src/openultrasast/rank.py — the static-evidence ranker (ranking_mode="evidence", existing-static-evidence-v1).
• src/openultrasast/mapping.py — builds the selected/deferred scope + the budget allocation that defers on deadline_exhausted.
• src/openultrasast/push/policy.py, push/runner.py — consume the scope; change_sites arrive via the push input.
• benchmarks/unseen/replay_entry.py — carries changed_paths / declaration_paths in the spec.

Increments (progress tracking)

• 0. Cost split confirmed (2026-10-11, timed live replay). build_seconds=66.69 (CPG build is cheap), query_seconds=0.0 (no query ever executed), total 303 s (hit the deadline), 4,095 candidate questions all analysis_not_started=deadline_exhausted, selected: [], regions_scanned=0. So of the 303 s, ~67 s built the CPG and ~236 s went to enumerating/ranking/preparing 4,095 repo-wide candidate questions for a 3-file change — the budget was spent before a single query ran. The CPG build is NOT the bottleneck; the over-broad candidate set is. Change-scoping is confirmed as the right lever, and because per-candidate prep includes the line-correspondence work, fewer candidates also cuts that cost (the two fixes compound).
• 1. Located (2026-10-11). The over-enumeration is upstream of the scan, not inside it:
  • push/runner.py:345-348 — head_regions = discovery(tip.root, …) discovers regions across the whole head tree (the ~4,095), independent of the change.
  • push/runner.py:397-407 — those regions are passed wholesale to scan_repository(..., change_context=context); change_context is threaded in but never scopes the region set.
  • model/scan.py:167 and :364 — [_identity(unit, r, f) for r in regions for f in r.families] enumerates all regions × families; with order_by_evidence=True (runner.py:288/403) the scan evidence-tiers every pair before analysis — that pre-work is the ~236 s measured in increment 0, so the deadline hits at query_seconds=0.
  • Primitive already present: model/regions.py:279 computes the changed-path set from the context; compare_targeted_base (runner.py:411) is a precedent for change-targeted work.
  • Fix locus (increment 2): restrict the analyzed region set to the change's declarations + dataflow neighbourhood (filter head_regions by context.changed_paths before the scan, or scope the enumeration inside scan_repository). Keep the CPG whole-repo so taint queries can still trace sources from unchanged code into the changed regions — only the question set is change-scoped.
• 2. Seed analysis from the change. Prioritize (or restrict first-pass to) the changed declarations plus their call/data neighborhood over repo-wide static rank, within budget.
• 3. Soundness. Keep cross-file dataflow INTO the changed function (don't scope to literal changed lines); add tests; keep existing push tests green.
• 4. Live re-test. Re-run the bedrock change: does analysis now RUN and produce a finding on install_packages (vs deferred)? Re-run metrics-plugin.
• 5. Measure the delta. Engine-arm sample on the pool (catch/false-alarm + engine-coverage rate) — needs the kube lane (blocked on the AX completion path) for scale.

Dependencies / notes

• Coverage complete_within_scope also needs the capability registry (Task 8.3) regardless of this work; the win here is findings moving from comparison_unknown/deferred to a decided novelty → advisory engine finding.
• Implement directly and verify on the LIVE instrument (a fake-client/unit pass is not evidence the live engine changed); the kube-lane and store endpoints are operator-owned infra, not assumptions to make here.