saqib mirandClaude Sonnet 5
d65683641a
Wires the pre-scaffolded, unused platform/reports module (ReportsService
.generateSummaryReport previously returned {}) into four real, admin-
gated dashboards matching docs/09-testing-observability-cicd.md's own
table:
- GET /admin/reports/management: total cases, AI-resolved, human-
escalated, resolved/open, SLA compliance/breaches, escalation count,
average response/resolution time.
- GET /admin/reports/product/:externalProductId: support volume,
problem-category breakdown, recurring problems, AI-resolution/human-
escalation rate, top error codes.
- GET /admin/reports/support: current per-agent workload, SLA at-risk/
breached counts, escalation count, response/resolution performance.
- GET /admin/reports/ai: AI resolution/human-handoff rate, failed-
troubleshooting-then-escalated rate, knowledge-match rate, confidence
distribution (reusing 005-ai-support's own decideConfidenceBand),
tool invocation success/failure.
Every rate/average is number|null -- null means no qualifying data in
range, never a computed NaN or a misleading 0. Adds one new durable
table, ErrorCodeLookup, since 014-full-observability's own equivalent
metric is a process-lifetime Prometheus counter unusable for a
historical "top errors" report.
Verified end-to-end against real Postgres/Redis: every figure checked
against hand-computed expected values, including a no-activity range
(all-zero counts, all-null rates) and cross-product isolation.
Also fixes a real regression the new ErrorCodeLookup FK caused in the
pre-existing known-issues.test.ts (its afterAll deleted ErrorCode rows
before the now-referencing lookup rows).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
30 lines
1.2 KiB
TypeScript
30 lines
1.2 KiB
TypeScript
import { describe, it, expect } from 'vitest';
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import { decideConfidenceBand } from '@/modules/ai-support/sessions';
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import { aiConfig } from '@/config';
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/**
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* 015-reporting-dashboards research.md §7: the AI dashboard's confidence distribution reuses
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* 005-ai-support's own decideConfidenceBand against the system-default thresholds, rather than
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* reimplementing a threshold check — this test proves the reused function classifies values
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* the way the dashboard's own bucketing loop (reports.service.ts) depends on.
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*/
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describe('AI dashboard confidence distribution reuses decideConfidenceBand', () => {
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const policy = {
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highThreshold: aiConfig.defaultHighConfidence,
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lowThreshold: aiConfig.defaultLowConfidence,
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};
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it('classifies a high-confidence value as proceed', () => {
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expect(decideConfidenceBand(policy.highThreshold, policy)).toBe('proceed');
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});
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it('classifies a low-confidence value as escalate', () => {
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expect(decideConfidenceBand(policy.lowThreshold - 0.01, policy)).toBe('escalate');
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});
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it('classifies a mid-range value as ask', () => {
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const midpoint = (policy.highThreshold + policy.lowThreshold) / 2;
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expect(decideConfidenceBand(midpoint, policy)).toBe('ask');
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});
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});
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