phase: 7, status: plan-as-execute, persona: backend-engineer+security-engineer, task: T-7.9
---ci--- project: acdl phase: 7 milestone: v1.1 status: plan-as-execute persona: backend-engineer+security-engineer task: T-7.9 requirements.covered: [REQ-19] ---/ci--- Wave 4 (co-authored): platform/confidence_signal.py (REQ-19). backend-engineer: 6-input weighted sum (WEIGHTS = policy 0.30, validation 0.25, freshness 0.10, source 0.15, history 0.10, nfrs 0.10 per D-040; sums to 1.0), per-env thresholds (dev 0.50, qa 0.75, prod 0.90, dr 0.95), _per_input_score rules, compute() orchestration, Signal dataclass, dev-warn->block flip (autonomous, no reviewer). security-engineer: PENALTY dict (critical=None hard-override sentinel, high 0.20, medium 0.05, low 0.01, info 0.0), critical-override short-circuit return in compute(), severity penalty loop over the policy input's failing PolicyCheckResult records. Verified: missing input -> block + INPUT_MISSING:<name>; critical fail -> 0.0 block + CRITICAL_OVERRIDE:<ruleId>; cold-start spike dev (ACDL _TAG_NAMING skipped, all validation true, nfrs neutral 0.5) -> 0.95 pass. stdlib only.
This commit is contained in:
Jon Chery
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"""ACDL Confidence Signal (REQ-19).
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The platform's certified answer to "is this safe to proceed?" (vision
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tenet: "Safety is Computed, Not Assumed"). Every delivery action produces
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a measurable, explainable confidence signal; reliance on operator
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instinct is not a substitute.
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Inputs (weights sum to 1.0, D-040):
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1. policy_results (0.30) — list[PolicyCheckResult] (schemas/policy_check_result.schema.json)
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2. validation (0.25) — {schema: bool, ir_resolved: bool, tf_validated: bool, tf_planned: bool}
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3. freshness (0.10) — {age_days: float, max_age_days: float}
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4. source (0.15) — {submitter: str, commit_sha: str, signed: bool}
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5. history (0.10) — {prior_rollbacks: int, prior_policy_fails: int}
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6. nfrs (0.10) — {declared: list[str], conformance: float|None}
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Severity -> penalty (locked, ARCHITECTURE.md §8):
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critical -> hard override (score = 0, block)
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high -> -0.20
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medium -> -0.05
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low -> -0.01
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info -> 0.00
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Per-env thresholds (locked, ARCHITECTURE.md §8): dev 0.50, qa 0.75, prod 0.90, dr 0.95.
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Output: {score, band, perInput, reasonCodes}.
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Halt with explicit reason on missing input (§8).
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Spike cold-start (A-6.2): inputs 3 (freshness), 5 (history), 6 (nfrs) are
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'present + neutral 0.5' because the spike is the first submission with no
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history and no declared NFRs. The gate is *presence*, not *conformance* —
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the 'all six inputs present' dev gate (§5) is satisfied by non-null
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per-input scores.
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"""
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from dataclasses import dataclass, asdict
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from typing import List, Literal, Optional, Dict, Any
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import json
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import sys
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WEIGHTS = {
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"policy": 0.30,
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"validation": 0.25,
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"freshness": 0.10,
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"source": 0.15,
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"history": 0.10,
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"nfrs": 0.10,
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}
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PENALTY = {
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"critical": None,
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"high": 0.20,
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"medium": 0.05,
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"low": 0.01,
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"info": 0.0,
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}
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THRESHOLDS = {"dev": 0.50, "qa": 0.75, "prod": 0.90, "dr": 0.95}
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@dataclass
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class Signal:
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score: float
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band: Literal["pass", "warn", "block"]
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perInput: Dict[str, float]
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reasonCodes: List[str]
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def _per_input_score(name: str, raw: Any) -> tuple:
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"""Return (score in [0,1], reasons list). Unknown/missing -> 0.5 + INPUT_MISSING."""
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reasons: List[str] = []
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if raw is None:
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return 0.5, [f"INPUT_MISSING:{name}"]
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if name == "policy":
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pcrs = raw if isinstance(raw, list) else []
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if not pcrs:
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return 0.5, []
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scores = []
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for pcr in pcrs:
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r = pcr.get("result", "skipped")
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if r == "pass" or r == "skipped":
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scores.append(1.0)
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else:
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scores.append(0.0)
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return sum(scores) / len(scores), []
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if name == "validation":
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keys = ("schema", "ir_resolved", "tf_validated", "tf_planned")
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if not isinstance(raw, dict):
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return 0.5, []
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trues = sum(1 for k in keys if raw.get(k))
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return trues / 4.0, []
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if name == "freshness":
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if not isinstance(raw, dict):
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return 0.5, []
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age = float(raw.get("age_days", 0))
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mx = float(raw.get("max_age_days", 1)) or 1
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s = 1.0 - (age / mx)
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return max(0.0, min(1.0, s)), []
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if name == "source":
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if not isinstance(raw, dict):
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return 0.5, []
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if raw.get("submitter") and raw.get("commit_sha"):
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return 1.0, []
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return 0.5, []
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if name == "history":
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if not isinstance(raw, dict):
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return 0.5, []
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rollbacks = int(raw.get("prior_rollbacks", 0))
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fails = int(raw.get("prior_policy_fails", 0))
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s = 1.0 - (rollbacks * 0.2 + fails * 0.1)
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return max(0.0, min(1.0, s)), []
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if name == "nfrs":
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if not isinstance(raw, dict):
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return 0.5, []
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conf = raw.get("conformance")
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if conf is None:
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return 0.5, []
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return float(conf), []
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return 0.5, []
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def compute(contract_id: str, environment: str,
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inputs: Dict[str, Any]) -> Signal:
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"""Orchestrate the 6-input weighted sum + severity penalty + band."""
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missing = sorted(set(WEIGHTS.keys()) - set(inputs.keys()))
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if missing:
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return Signal(0.0, "block", {},
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[f"INPUT_MISSING:{m}" for m in missing])
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per_input: Dict[str, float] = {}
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reasons: List[str] = []
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base = 0.0
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for name, weight in WEIGHTS.items():
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raw = inputs.get(name)
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s, r = _per_input_score(name, raw)
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per_input[name] = s
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reasons.extend(r)
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base += s * weight
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penalty = 0.0
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policy_input = inputs.get("policy")
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pcrs = policy_input if isinstance(policy_input, list) else []
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for pcr in pcrs:
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if not isinstance(pcr, dict):
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continue
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if pcr.get("result") != "fail":
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continue
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sev = pcr.get("severity")
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p = PENALTY.get(sev, 0.0)
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if p is None:
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return Signal(0.0, "block", per_input,
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reasons + [f"CRITICAL_OVERRIDE:{pcr.get('ruleId','?')}"])
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penalty += p
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score = max(0.0, min(1.0, base - penalty))
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threshold = THRESHOLDS[environment]
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if score >= threshold:
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band = "pass"
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elif score < threshold - 0.10:
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band = "block"
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else:
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band = "warn"
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if environment == "dev" and band == "warn":
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band = "block"
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return Signal(score, band, per_input, reasons)
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if __name__ == "__main__":
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if len(sys.argv) < 3:
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print("usage: confidence_signal.py <inputs.json> <environment>", file=sys.stderr)
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sys.exit(2)
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env = sys.argv[2]
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with open(sys.argv[1], "r", encoding="utf-8") as fh:
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inputs = json.load(fh)
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sig = compute("cli", env, inputs)
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print(json.dumps(asdict(sig), indent=2))
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