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Marius CiepluchandClaude Opus 5.5
d1896cb8e7
learn/schema.py is the closed, typed allow-list of candidate features: 66 features over 11 instruments (language one-hot, quick rules with 23 mechanism buckets and a prior count, engine, repo-facts, source span, entry-point distance, roles, delta, verify, agree, model sinks), two profiles (static 57; plane adds verify, agree and model sinks for 66), schema_version and a feature-set digest. No repository, path, file, function, rule or commit identity is a feature; site_match, in_fix_range, fixed-pin alerts, case.json fields and costs are refused. Missing is not zero: each record carries per-instrument state (ran | none | failed | not_applicable), and every feature of an instrument that did not run is null. learn/features.py builds records on the host (build_for_scan, whole repository or base..head delta); plane/tasks/features.py builds them per case in a Run from the delivered artifacts, dropping site_match and in_fix_range on read and ignoring fixed-pin alerts. The generator adds a model-free `features` entry per case before `remember`, which stores the records as `features` rows (re-validated). memory KINDS gains features, label, decision, experiment, arm_outcome and experiment_result. The committed validation-46 manifests are regenerated (entries appended; the first Run's entries are unchanged). The runner image must be rebuilt and re-pinned before a Run executes the new task. Proof on real artifacts (scratch store, not committed): the recorded validation-46 run gives 75 feature records over 15 cases (43 verify candidates plus vulnerable-pin alerts), each case's vulnerable pin exported and read (0.5k-9k files); remember stored 75 features rows beside 43 verdict, 84 alert, 61 unit_cost, 15 facts and 8 coverage rows. Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
63 lines
3.4 KiB
Markdown
63 lines
3.4 KiB
Markdown
# Implementation Plan
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Order follows the design's commit sequence. Every task is a commit gated on the full suite's own exit code,
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`ruff check`, `ruff format --check` and `mypy`. No reported number changes until task 9 adopts a model; without an
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adopted model every path is today's. Tasks 5, 7 and 10 spend model money or host hours: each starts only after
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the maintainer confirms its budget.
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- [x] 1. Schema and features
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- `learn/schema.py` (closed, typed allow-list; no identities; missing != zero), `learn/features.py` (host
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builder), new memory `KINDS`, `plane/tasks/features.py` and the `remember` input; tests enforcing the
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allow-list and the exclusion of label-carrying fields.
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- _Requirements: 1.1, 1.2, 1.3, 7.2_
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- [ ] 2. Framework tags
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- `ruleset/frameworks.toml`; tag and split every framework/library entry listed in design section 2; `priors=`
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in the semantic and quick loaders (default `all`, so today's scan is unchanged -- the golden benchmark test
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stays byte-identical); a lint test that no untagged framework entry remains.
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- _Requirements: 8.2, 8.4_
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- [ ] 3. Role inference
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- `learn/roles.py` (deterministic wrapper inference), `plane/tasks/roles.py` (model roles, the `model_sinks.py`
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approach), the vocabulary overlay for the engine; fixture tests with no model calls.
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- _Requirements: 8.1_
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- [ ] 4. Labels
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- `learn/sources.toml` (fail-closed allowed sources, v3 excluded unread), `learn/labels.py`, `ousast learn
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labels`, repository-group normalisation and CVE merge, the `assumed_benign` source mined from non-security
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commits (separate, never merged); a first label snapshot committed as counts only.
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- _Requirements: 2.1, 2.2, 2.3, 7.1_
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- [ ] 5. Harvest (needs budget go-ahead)
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- A plane Run of verify a/b on the verify-family pairs and development cases (~$6, ceiling $10), model roles on
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the candidate files (ceiling $10), engine features for the pair corpus on the host (frozen source, one
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container at a time, background).
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- _Requirements: 1.1_
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- [ ] 6. Train, calibrate, evaluate
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- `learn/{folds,train,calibrate,evaluate}.py` (repository-grouped, leave-one-source-out, leave-one-framework-out,
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nested tuning), the model blob API in `plane/memory.py`, `ousast learn train|evaluate|curve`, the first
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evaluation report with priors off under `benchmarks/measurements/<date>-decision-engine-v1/`.
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- _Requirements: 3.1-3.5, 5.1, 5.2, 7.3, 7.4, 8.3_
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- [ ] 7. Experiments (exp-001 needs budget go-ahead)
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- `learn/experiments.py`, `register`, `experiment-outcome`, Run annotation checks, `OUSAST_VERIFY_CALLERS`;
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exp-001 (known-callers line on/off, ~$7.7 expected, ceiling $15) registered, run and recorded -- an
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inconclusive result is pre-registered as likely.
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- _Requirements: 4.1-4.4_
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- [ ] 8. Integration
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- The `decide` task, the `scan` decision stage and `dropped_by_engine.json`, `pre-push` admission with the engine
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(structural admission reasons unchanged), report fields, the fallback line, the opt-in adaptation layer.
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- _Requirements: 6.1-6.3, 7.3, 7.5_
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- [ ] 9. Adopt
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- `ousast learn adopt`, packaged models for the families that pass, the priors-on/off experiment per framework
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with a recorded decision.
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- _Requirements: 3.2, 8.2_
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- [ ] 10. The one-time v3 check
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- Commit `prediction-v3.json` first; then v3 is run under protocol v3 and `result-v3-engine.json` is committed.
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Nothing between the two commits may touch the engine, features, priors or thresholds.
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- _Requirements: 5.3_
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