Slopmeter v0.5.0

Slopmeter v0.5.0

This release adds attributed review history, experimental stored-count analysis,
and coverage summaries that show what the experiments actually assessed.
Calibration, thresholds, scoring formulas, pattern rules, and clone detection
remain unchanged.

Experimental coverage and explanations

models, variants, and derived now put coverage before detailed findings:
files analyzed/failed, subjects encountered/assessed/unresolved, findings, and
grouped unresolved reasons. JSON includes the same computed summary.

Nothing assessed differs from assessed subjects with no findings. Class counts
include ordinary classes and test fakes; they do not measure business-model
coverage or recall. Failed files contribute no inferred subject count. Missing
and fallback variant handlers count as findings; exhaustive handlers do not prove
branch correctness.

Variant explanations now identify unsupported scope/signatures, non-leading
matches, subject shapes, annotations, bindings, declarations, and patterns.
These explanations preserve the supported syntax and successful evidence.

Stored-count review

slop derived --root . --lang py
slop derived --root C:/project --config C:/reviews/project.toml --json

This experimental command connects a stored len(items), a length-increasing
list mutation, and a later read without recomputation. It supports straight-line
local list literals with append, nonempty literal extend, and insert.
Unknown calls, aliases, control flow, object fields, and uncertain bindings remain
unresolved. A stored count can be an intentional snapshot; review the consumer's
contract. The command never executes source and makes no score or M2 contribution.

Attributed review history

slop score . --lang py --json | Set-Content -Encoding utf8 snapshot.json
slop review-report list --report snapshot.json
slop review-report set TARGET_ID --report snapshot.json --store decisions.json --actor reviewer-name --disposition defer --reason "Review when the contract changes." --next-step "Check failure paths."
slop review-report show --report snapshot.json --store decisions.json

The new workflow accepts saved JSON from score, models, variants, and
derived. It retains clone, complexity, pattern, model, variant, and derived-state
decisions with an explicit actor, UTC timestamp, rationale, next step, and history.
Failed and unresolved experimental results are not review targets.

These commands inspect saved reports. Generate a fresh report to check current
source. Reads do not write stores, and changes are never automatically approved.
New clone decisions bind their members' effective boundary assignments; unrelated
declarations do not invalidate them. Changed member assignments, source bytes,
evidence, or analysis definitions still do. Comment-only edits still require review.

Fixes and compatibility

• Model review rejects class-local bool shadowing instead of claiming a built-in
  Boolean field. Supported unshadowed evidence is unchanged.
• Findings from historical M4 v1/v2 reports use full complexity. M4 v3 uses the
  cohort-specific basis. Missing or unknown versions do not invent classifications.
• Default calibration remains py-2026.3, M4 version 3, with the same six historical
  reference projects. No project-specific score tuning was introduced.
• The current reader accepts old experimental reports without a summary and
  validates supplied summaries against detailed evidence. Older readers may reject
  the new additive summary field; upgrade readers before consuming new reports.
• Schema-1 clone stores remain readable. The first explicit review-report set
  write preserves old decisions under legacy_decisions in a schema-2 ledger.
  Legacy decisions retain whole-policy invalidation. Use review-report afterward;
  the original review commands remain schema-1-only. Keep a copy for legacy use.
• Review evidence, source selection, profile identity, and scan root must stay
  comparable. Keep separate stores for independent roots.

Validation

• 1,574 deterministic tests passed with 95.20% branch-inclusive coverage; four
  clean wheel/source installation checks passed separately.
• All 69 new coverage/explanation cases passed on Python 3.13 and 3.14.
• Ruff, Pyright, and all five import contracts passed.
• On identical final source, ordinary score JSON matched the pre-change
  implementation exactly before the version bump. Experimental evidence matched
  after removing only summaries and unresolved variant reason text.
• Independent HTTPX/Rich/Black evaluation supplied no eligible positive cases.
  Self-scans likewise expose narrow experimental coverage. These results do not
  establish detector precision, recall, or a general correctness guarantee.

Use the tool for advisory review. Assets include a wheel, source distribution,
and SHA-256 checksums.

Hosted release CI passed all nine Windows/macOS/Linux and Python 3.12–3.14 jobs, plus static checks and dependency audit: CI run.

Source commit: 3306dc0.