generated from coulomb/repo-seed
Bernd Worsch
0f505feb2d
Closes the long-range feedback loop: outcome signals now enrich the full
traceability chain and feed back into routing, triage, and AI proposals.
Schema (T01):
- outcome_correlations (CHECK correlation_type)
- pattern_performance_records
- adaptive_threshold_configs
- institutional_knowledge_entries (GIN tsvector FTS)
- learning_insights (CHECK insight_type)
- ALTER TABLE decision_records + requirement_candidates: outcome_summary JSONB
- AFTER INSERT trigger trg_enrich_lineage on outcome_signals
- contracts/core/ updated (outcome-summary-columns-v1, append-only addendum)
Correlation engine (T02):
- Application/Helper/CorrelationEngine.hs: pure annotation→outcome SQL
- Web/Controller/OutcomeCorrelations.hs: ComputeCorrelationsAction + index
Pattern performance (T03):
- Web/Controller/PatternPerformance.hs: ComputePatternPerformanceAction
Adaptive thresholds (T04):
- Web/Controller/AdaptiveThresholds.hs: CalibrateThresholdsAction
- Application/Helper/FrictionScore.hs: applyAdaptiveWeights
Institutional knowledge (T05):
- DistilDecisionAction in DecisionRecords controller
- Web/Controller/InstitutionalKnowledge.hs: QueryKnowledgeBaseAction
Lineage enrichment (T06):
- Web/Controller/LineageEnrichment.hs: EnrichLineageAction (batch backfill)
- enrich_lineage_on_outcome_batch() PL/pgSQL helper in migration
Learning dashboard (T07):
- Web/Controller/LearningDashboard.hs: 5-panel autoRefresh view
- "Learning" nav link in FrontController
API v2 learning endpoints (T08):
- GET /api/v2/outcome-correlations, /pattern-performance, /knowledge-base/{id}
- OpenAPI schemas: OutcomeCorrelation, PatternPerformanceRecord, InstitutionalKnowledgeEntry
GAAF scorecard + docs (T09):
- Core 3.8→3.9, Functional 3.6→3.8, overall 3.61→3.68
- CLAUDE.md: IHF v0.2 complete, no active workplan
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
99 lines
4.0 KiB
Haskell
99 lines
4.0 KiB
Haskell
module Application.Helper.FrictionScore where
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import IHP.Prelude
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import IHP.ModelSupport
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import Generated.Types
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import Data.Time.Clock (addUTCTime, getCurrentTime)
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import qualified Data.Aeson as A
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import qualified Data.HashMap.Strict as H
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-- | Friction score formula (documented):
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--
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-- score = min 100 $
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-- annotationCount * 5
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-- + errorEventCount * 10
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-- + (if regressionFlag then 20 else 0)
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-- + staleCandidateCount * 8
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--
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-- Inputs are computed from the widget's related records.
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computeFrictionScore
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:: (?modelContext :: ModelContext)
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=> Id Widget
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-> [Annotation] -- all annotations for this widget
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-> [InteractionEvent] -- all events for this widget
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-> Bool -- True if widget is in regression
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-> [RequirementCandidate] -- all candidates for this widget
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-> IO FrictionScore
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computeFrictionScore wid annotations events isRegressed candidates = do
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now <- getCurrentTime
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let thirtyDaysAgo = addUTCTime (negate $ 30 * 86400) now
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annCount = length annotations
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errCount = length (filter (\e -> e.eventType == "errored") events)
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staleCount = length (filter (\c -> c.status == "open" && c.createdAt < thirtyDaysAgo) candidates)
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rawScore = annCount * 5 + errCount * 10 + (if isRegressed then 20 else 0) + staleCount * 8
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finalScore = min 100 rawScore
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-- Upsert: update if row exists, insert otherwise
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existingRows <- sqlQuery
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"SELECT * FROM friction_scores WHERE widget_id = ? LIMIT 1"
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(Only wid)
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case (existingRows :: [FrictionScore]) of
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(existing : _) -> do
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existing
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|> set #score finalScore
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|> set #annotationCount annCount
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|> set #errorEventCount errCount
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|> set #regressionFlag isRegressed
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|> set #staleCandidateCount staleCount
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|> set #lastComputedAt now
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|> updateRecord
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[] -> do
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newRecord @FrictionScore
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|> set #widgetId wid
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|> set #score finalScore
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|> set #annotationCount annCount
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|> set #errorEventCount errCount
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|> set #regressionFlag isRegressed
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|> set #staleCandidateCount staleCount
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|> set #lastComputedAt now
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|> createRecord
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-- | Score band for Tailwind colour coding.
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scoreBand :: Int -> Text
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scoreBand s
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| s < 20 = "bg-green-100 text-green-800"
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| s < 40 = "bg-yellow-100 text-yellow-800"
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| s < 60 = "bg-orange-100 text-orange-800"
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| otherwise = "bg-red-100 text-red-800"
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-- | Read per-hub AdaptiveThresholdConfig and apply weight_overrides
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-- to friction component scores before summing. Falls back to global
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-- defaults when no config exists for the hub.
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-- weight_overrides keys: "annotation", "error", "regression", "stale"
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applyAdaptiveWeights ::
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(?modelContext :: ModelContext) =>
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Id Hub ->
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Int -> -- annotationCount
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Int -> -- errorEventCount
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Bool -> -- regressionFlag
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Int -> -- staleCandidateCount
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IO Int
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applyAdaptiveWeights hubId annCount errCount isRegressed staleCount = do
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mConfig <- query @AdaptiveThresholdConfig
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|> filterWhere (#hubId, hubId)
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|> fetchOneOrNothing
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let overrides = maybe mempty (.weightOverrides) mConfig
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w k def = case overrides of
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A.Object o -> case H.lookup k o of
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Just (A.Number n) -> round (n * fromIntegral def) :: Int
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_ -> def
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_ -> def
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annW = w "annotation" 5
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errW = w "error" 10
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regW = w "regression" 20
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staleW = w "stale" 8
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raw = annCount * annW
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+ errCount * errW
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+ (if isRegressed then regW else 0)
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+ staleCount * staleW
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pure (min 100 raw)
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