initial commit

packages/summarizer/scoring.test.ts

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+import { expect, test } from "bun:test";
+import { SCORING_MODEL } from "./scoring-model";
+import { calculateScoringResult, deriveTrafficLight, type LlmScoringAssessment } from "./scoring";
+
+function assessmentWithColor(farbe: "gruen" | "gelb" | "rot" | "unbewertbar"): LlmScoringAssessment {
+  return {
+    ausschlussgruende: [],
+    dimensionen: SCORING_MODEL.map((dimension) => ({
+      id: dimension.id,
+      subcriteria: dimension.subcriteria.map((subcriterion) => ({
+        id: subcriterion.id,
+        farbe,
+        evidence: farbe === "unbewertbar" ? "" : "Test evidence",
+        begruendung: "Test begruendung",
+        confidence: 0.9,
+        missingReason: farbe === "unbewertbar" ? "Keine belastbaren Angaben" : "",
+      })),
+    })),
+  };
+}
+
+test("calculates a green weighted score when all criteria are green", () => {
+  const scoring = calculateScoringResult(SCORING_MODEL, assessmentWithColor("gruen"));
+
+  expect(scoring.gesamtScore).toBe(100);
+  expect(scoring.farbe).toBe("gruen");
+  expect(scoring.unbewertbareKriterien).toBe(0);
+});
+
+test("scores against the maximum achievable by assessable dimensions", () => {
+  const assessment = assessmentWithColor("gruen");
+  for (const subcriterion of assessment.dimensionen[4]!.subcriteria) {
+    subcriterion.farbe = "unbewertbar";
+    subcriterion.evidence = "";
+    subcriterion.missingReason = "Keine belastbaren Angaben";
+  }
+
+  const scoring = calculateScoringResult(SCORING_MODEL, assessment);
+
+  expect(scoring.gesamtScore).toBe(100);
+  expect(scoring.farbe).toBe("gruen");
+  expect(scoring.dimensionen[4]!.score).toBe(0);
+  expect(scoring.dimensionen[4]!.scorableWeight).toBe(0);
+  expect(scoring.unbewertbareKriterien).toBeGreaterThan(0);
+});
+
+test("normalizes a partially assessable dimension by answered criterion weight", () => {
+  const assessment = assessmentWithColor("gruen");
+  const innovation = assessment.dimensionen.find((dimension) => dimension.id === "innovation")!;
+  for (const subcriterion of innovation.subcriteria) {
+    if (subcriterion.id === "innovation_output") {
+      subcriterion.farbe = "unbewertbar";
+      subcriterion.evidence = "";
+      subcriterion.missingReason = "Keine belastbaren Angaben";
+    }
+  }
+
+  const scoring = calculateScoringResult(SCORING_MODEL, assessment);
+  const innovationScore = scoring.dimensionen.find((dimension) => dimension.id === "innovation")!;
+
+  expect(innovationScore.scorableWeight).toBe(75);
+  expect(innovationScore.score).toBe(100);
+  expect(scoring.gesamtScore).toBe(100);
+  expect(scoring.farbe).toBe("gruen");
+});
+
+test("derives red when automatic exclusion reasons are present", () => {
+  const scoring = calculateScoringResult(SCORING_MODEL, assessmentWithColor("gruen"));
+
+  expect(deriveTrafficLight(scoring, ["Stiftung als Bewerber"])).toBe("rot");
+});
+
+test("keeps low-scoring non-excluded applications yellow rather than discarded", () => {
+  const scoring = calculateScoringResult(SCORING_MODEL, assessmentWithColor("rot"));
+
+  expect(scoring.gesamtScore).toBe(0);
+  expect(scoring.farbe).toBe("gelb");
+  expect(deriveTrafficLight(scoring, [])).toBe("gelb");
+});