588 lines
27 KiB
TypeScript
588 lines
27 KiB
TypeScript
import { describe, expect, it } from 'vitest';
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import type { CanonicalAssessmentModel } from '../../src/domain/canonical-model';
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import { canonicalModelToDataset, canonicalModelsToDataset, pingCastleSectionSheetId } from '../../src/features/project-import/canonical-model-to-dataset';
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import { linkResultSheets } from '../../src/profiles/purple-knight/result-linker';
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import { linkPingCastleSectionSheets, pingCastleSectionIds } from '../../src/profiles/pingcastle/pingcastle-section-links';
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const timestamp = '2026-07-18T08:00:00.000Z';
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function model(): CanonicalAssessmentModel {
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return {
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schemaVersion: 1,
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project: { id: 'project-1', name: 'Test', createdAt: timestamp, updatedAt: timestamp, attributes: {} },
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assessment: { id: 'assessment-1', projectId: 'project-1', name: 'Assessment', createdAt: timestamp, updatedAt: timestamp },
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scanRuns: [{ id: 'run-1', assessmentId: 'assessment-1', sequence: 1, label: 'Import 1', tool: { id: 'purple-knight', name: 'PurpleKnight' }, importedAt: timestamp }],
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importFiles: [{
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id: 'file-1', scanRunId: 'run-1', fileName: 'Indicators_results.csv', format: 'CSV', sizeBytes: 100,
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importedAt: timestamp, detectedTool: { id: 'purple-knight', name: 'PurpleKnight' },
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detection: { level: 'high', score: .9, reasons: [] },
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processedWith: { parserId: 'csv', parserVersion: '1', profileId: 'purple-knight', profileVersion: '1', mappingVersion: '1' },
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sourceArtifact: { kind: 'not-retained', reason: 'test' }
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}],
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sourceTables: [{ id: 'table-1', importFileId: 'file-1', name: 'Indicators', index: 0, rowCount: 1, columnCount: 2, attributes: {} }],
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findingDefinitions: [{ id: 'definition-1', canonicalKey: 'pk:1', title: 'Finding', frameworkReferences: [], createdAt: timestamp, updatedAt: timestamp }],
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findingOccurrences: [{
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id: 'occurrence-1', definitionId: 'definition-1', scanRunId: 'run-1', importFileId: 'file-1', sourceTableId: 'table-1',
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sourceIdentity: { tool: { id: 'purple-knight', name: 'PurpleKnight' }, originalRuleId: 'PK-1', originalTitle: 'Finding' },
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sourcePosition: { rowIndex: 0 },
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originalFields: [{ key: 'rule', label: 'Rule ID', value: 'PK-1', columnIndex: 0 }, { key: 'title', label: 'Title', value: 'Finding', columnIndex: 1 }],
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fieldMappings: [],
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severity: { originalValue: 'High', normalizedValue: 'high', provenance: { decisionSource: 'automatic', confidence: { level: 'high', score: .9, reasons: [] }, mappingVersion: '1' } },
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status: { originalValue: 'Open', normalizedValue: 'open', provenance: { decisionSource: 'automatic', confidence: { level: 'high', score: .9, reasons: [] }, mappingVersion: '1' } }
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}],
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evidence: [], reviews: [], diagnostics: []
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};
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}
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describe('canonical project loading for Work', () => {
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it('reconstructs a usable dataset from a stored assessment model', () => {
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const dataset = canonicalModelToDataset(model());
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expect(dataset).toMatchObject({ fileName: 'Test · All Scans', scanTool: 'PurpleKnight', profileId: 'purple-knight' });
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expect(dataset?.sheets[0].analysis.records).toHaveLength(1);
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expect(dataset?.sheets[0].analysis.records[0].title).toBe('Finding');
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expect(dataset?.sheets[0].analysis.headers).toEqual(['Source: File', 'Source: Sheet', 'Source: Scan', 'Rule ID', 'Title', 'Severity']);
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expect(dataset?.sheets[0].analysis.records[0].fields.find(field => field.label === 'Severity')?.value).toBe('high');
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});
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it('reconstructs readable CIS-CAT details and exact source-table links from structured originals', () => {
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const stored = model();
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stored.scanRuns[0].tool = { id: 'cis-cat', name: 'CIS-CAT Pro' };
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stored.importFiles[0] = {
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...stored.importFiles[0],
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fileName: 'CIS-CAT-SYNTHETIC.html',
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format: 'HTML',
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detectedTool: { id: 'cis-cat', name: 'CIS-CAT Pro' },
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processedWith: {
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...stored.importFiles[0].processedWith,
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profileId: 'cis-cat',
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profileVersion: '1.2.0',
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mappingVersion: '1.1.0'
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}
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};
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stored.sourceTables[0] = { ...stored.sourceTables[0], name: 'assessmentResultTable', index: 0 };
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stored.sourceTables.push({
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id: 'table-evidence', importFileId: 'file-1', name: 'Synthetic Item', index: 1, rowCount: 1, columnCount: 2,
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attributes: {},
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rows: [{
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index: 1,
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fields: [
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{ key: 'column-0', label: 'Name', value: 'Setting', columnIndex: 0 },
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{ key: 'column-1', label: 'Value', value: 'Disabled', columnIndex: 1 }
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]
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}]
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});
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stored.findingOccurrences[0] = {
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...stored.findingOccurrences[0],
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sourceIdentity: {
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tool: { id: 'cis-cat', name: 'CIS-CAT Pro' },
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originalRuleId: '1.1.1',
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canonicalRuleId: '1.1.1',
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originalTitle: '1.1.1 Ensure the synthetic setting is enabled'
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},
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originalFields: [
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{ key: 'rule-id', label: 'Rule ID', value: '1.1.1', columnIndex: 0 },
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{ key: 'benchmark-item', label: 'Benchmark Item', value: '1.1.1 Ensure the synthetic setting is enabled', columnIndex: 1 },
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{ key: 'result', label: 'Result', value: 'Fail', columnIndex: 2 },
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{
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key: 'results', label: 'Results',
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value: { fragmentId: 'detail-synthetic', tables: [{ tableIndex: 1, label: 'Assessment Evidence · Synthetic Item' }] },
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rawValue: 'source-table:1 "Assessment Evidence · Synthetic Item"'
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},
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{
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key: 'rule-result-xml', label: 'Rule Result XML',
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value: { fragmentId: 'detail-synthetic', ruleResultXml: '<result>fail</result>' },
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rawValue: '```xml\n<result>fail</result>\n```'
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}
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]
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};
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const dataset = canonicalModelToDataset(stored)!;
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const findingSheet = dataset.sheets.find(sheet => sheet.sourceSheet === 'assessmentResultTable')!;
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const finding = findingSheet.analysis.records[0];
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const field = (label: string) => finding.fields.find(item => item.label === label)?.value;
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expect(field('Results')).toBe('source-table:1 "Assessment Evidence · Synthetic Item"');
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expect(field('Rule Result XML')).toContain('<result>fail</result>');
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expect(field('Rule Result XML')).not.toContain('"fragmentId"');
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expect(linkResultSheets(finding, dataset.sheets, findingSheet.id)).toMatchObject([
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{ name: 'Assessment Evidence · Synthetic Item', sourceTableIndex: 1 }
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]);
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});
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/**
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* Regression: CIS-CAT markiert in einer Belegtabelle die konkret
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* ausgewertete Zeile selbst (`<tr class="evaluated">`). Diese Markierung
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* wurde beim Import erkannt, aber beim Aufbau der persistierten
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* Quelltabelle (`SourceTableRow`) verworfen - jede spaeter aus der
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* Datenbank geladene Projektansicht zeigte deshalb keine Markierung mehr,
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* obwohl der frische Import sie noch hatte.
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*/
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it('bewahrt die vom Bericht markierte Zeile ueber die persistierte Quelltabelle hinweg', () => {
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const stored = model();
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stored.sourceTables[0] = { ...stored.sourceTables[0], name: 'assessmentResultTable', index: 0 };
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stored.sourceTables.push({
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id: 'table-evidence', importFileId: 'file-1', name: 'Registry Item', index: 1, rowCount: 2, columnCount: 2,
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attributes: {},
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rows: [
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{
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index: 0,
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fields: [
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{ key: 'column-0', label: 'Name', value: 'Hive', columnIndex: 0 },
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{ key: 'column-1', label: 'Value', value: 'HKEY_LOCAL_MACHINE', columnIndex: 1 }
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],
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highlighted: false
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},
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{
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index: 1,
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fields: [
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{ key: 'column-0', label: 'Name', value: 'Value', columnIndex: 0 },
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{ key: 'column-1', label: 'Value', value: '5', columnIndex: 1 }
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],
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highlighted: true
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}
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]
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});
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const dataset = canonicalModelToDataset(stored)!;
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const evidenceSheet = dataset.sheets.find(sheet => sheet.sourceSheet === 'Registry Item')!;
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expect(evidenceSheet.analysis.records.map(record => ({
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name: record.fields.find(field => field.label === 'Name')?.value,
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highlighted: record.highlighted ?? false
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}))).toEqual([
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{ name: 'Hive', highlighted: false },
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{ name: 'Value', highlighted: true }
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]);
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});
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it('combines synthetic CSV and Excel assessments from the same tenant', () => {
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const csvModel = model();
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const excelModel = model();
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excelModel.assessment = { ...excelModel.assessment, id: 'assessment-2', name: 'Synthetic Excel assessment' };
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excelModel.scanRuns = [{ ...excelModel.scanRuns[0], id: 'run-2', assessmentId: 'assessment-2', importedAt: '2026-07-17T08:00:00.000Z' }];
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excelModel.importFiles = [{ ...excelModel.importFiles[0], id: 'file-2', scanRunId: 'run-2', fileName: 'synthetic-history.xlsx', format: 'Excel' }];
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excelModel.sourceTables = [{ ...excelModel.sourceTables[0], id: 'table-2', importFileId: 'file-2', name: 'Synthetic History' }];
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excelModel.findingDefinitions = [{ ...excelModel.findingDefinitions[0], id: 'definition-2', canonicalKey: 'pk:2', title: 'Synthetic historical finding' }];
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excelModel.findingOccurrences = [{
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...excelModel.findingOccurrences[0],
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id: 'occurrence-2',
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definitionId: 'definition-2',
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scanRunId: 'run-2',
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importFileId: 'file-2',
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sourceTableId: 'table-2',
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sourceIdentity: { ...excelModel.findingOccurrences[0].sourceIdentity, originalRuleId: 'PK-2', originalTitle: 'Synthetic historical finding' },
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originalFields: [
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{ key: 'rule', label: 'Rule ID', value: 'PK-2', columnIndex: 0 },
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{ key: 'title', label: 'Title', value: 'Synthetic historical finding', columnIndex: 1 }
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]
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}];
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const dataset = canonicalModelsToDataset([csvModel, excelModel]);
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expect(dataset).toMatchObject({ fileName: 'Test · All Scans', scanTool: 'PurpleKnight' });
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expect(dataset?.sheets[0].analysis.records).toHaveLength(2);
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expect(dataset?.sheets).toHaveLength(3);
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expect(dataset?.sheets.some(sheet => sheet.sourceFile === 'synthetic-history.xlsx')).toBe(true);
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});
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it('restores persisted PurpleKnight result tables and links them to the finding', () => {
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const stored = model();
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stored.findingOccurrences[0].originalFields.push({
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key: 'result', label: 'Result', value: 'SI000055', columnIndex: 2
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});
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stored.sourceTables.push({
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id: 'table-results', importFileId: 'file-1', name: 'SI000055', index: 1, rowCount: 1, columnCount: 4,
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attributes: { sourceFile: 'assessment.xlsx', sourceSheet: 'SI000055' },
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rows: [{
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index: 1,
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fields: [
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{ key: 'column-0', label: 'Attribute', value: 'nTSecurityDescriptor', columnIndex: 0 },
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{ key: 'column-1', label: 'DistinguishedName', value: 'CN=Synthetic', columnIndex: 1 },
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{ key: 'column-2', label: 'EventTimestamp', value: '2026-07-18T08:00:00Z', columnIndex: 2 },
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{ key: 'column-3', label: 'Ignored', value: false, columnIndex: 3 }
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]
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}]
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});
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const dataset = canonicalModelToDataset(stored)!;
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const findingSheet = dataset.sheets.find(sheet => sheet.sourceSheet === 'Indicators')!;
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const resultSheet = dataset.sheets.find(sheet => sheet.sourceSheet === 'SI000055')!;
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expect(resultSheet.analysis.records).toHaveLength(1);
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expect(linkResultSheets(findingSheet.analysis.records[0], dataset.sheets, findingSheet.id)).toEqual([resultSheet]);
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});
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it('creates a selectable file view for native findings without a source table', () => {
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const native = model();
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native.scanRuns = [{
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...native.scanRuns[0],
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tool: { id: 'pingcastle', name: 'PingCastle' }
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}];
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native.importFiles = [{
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...native.importFiles[0],
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fileName: 'ad_hc_synthetic.example.html',
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format: 'HTML',
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detectedTool: { id: 'pingcastle', name: 'PingCastle' },
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processedWith: {
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...native.importFiles[0].processedWith,
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parserId: 'html',
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profileId: 'pingcastle'
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}
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}];
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native.sourceTables = [];
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native.findingOccurrences = [{
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...native.findingOccurrences[0],
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sourceTableId: undefined,
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sourcePosition: { objectPath: 'html/rules/S-Synthetic' },
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sourceIdentity: {
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tool: { id: 'pingcastle', name: 'PingCastle' },
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originalRuleId: 'S-Synthetic',
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originalTitle: 'Synthetic PingCastle finding'
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}
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}];
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const dataset = canonicalModelToDataset(native);
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expect(dataset?.sheets).toHaveLength(2);
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expect(dataset?.sheets[1]).toMatchObject({
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name: 'PingCastle · 2026-07-18 · HTML',
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sourceFile: 'ad_hc_synthetic.example.html',
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sourceSheet: 'HTML'
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});
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expect(dataset?.sheets[1].analysis.records).toHaveLength(1);
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});
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it('keeps PingCastle HTML and XML findings separate even when file stem and Rule ID match', () => {
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const html = model();
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html.scanRuns = [{ ...html.scanRuns[0], tool: { id: 'pingcastle', name: 'PingCastle' } }];
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html.importFiles = [{
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...html.importFiles[0],
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fileName: 'ad_hc_synthetic.example.html',
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format: 'HTML',
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detectedTool: { id: 'pingcastle', name: 'PingCastle' }
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}];
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html.sourceTables = [];
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html.findingOccurrences = [{
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...html.findingOccurrences[0],
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sourceTableId: undefined,
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sourceIdentity: {
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tool: { id: 'pingcastle', name: 'PingCastle' },
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originalRuleId: 'S-Synthetic',
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canonicalRuleId: 'S-Synthetic',
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originalTitle: 'Synthetic finding'
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},
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originalFields: [
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{ key: 'risk-id', label: 'Rule ID', value: 'S-Synthetic' },
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{ key: 'title', label: 'Title', value: 'Synthetic finding' },
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{ key: 'description', label: 'Description', value: 'Synthetic manufacturer description.' },
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{ key: 'technical', label: 'Technical explanation', value: 'Synthetic technical explanation.' },
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{ key: 'solution', label: 'Advised solution', value: 'Synthetic remediation.' },
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{ key: 'documentation', label: 'Documentation', value: 'https://example.invalid/rule' },
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{
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key: 'linked-section',
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label: 'Verwandte Daten',
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value: {
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sections: [{
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sectionId: 'syntheticsection',
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sectionLabel: 'Synthetic Section',
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prose: '',
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headers: ['Name'],
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rows: [['Synthetic Evidence']]
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}]
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}
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}
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]
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}];
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const xml = model();
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xml.assessment = { ...xml.assessment, id: 'assessment-xml' };
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xml.scanRuns = [{
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...xml.scanRuns[0],
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id: 'run-xml',
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assessmentId: 'assessment-xml',
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tool: { id: 'pingcastle', name: 'PingCastle' }
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}];
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xml.importFiles = [{
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...xml.importFiles[0],
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id: 'file-xml',
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scanRunId: 'run-xml',
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fileName: 'ad_hc_synthetic.example.xml',
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format: 'XML',
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detectedTool: { id: 'pingcastle', name: 'PingCastle' }
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}];
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xml.sourceTables = [];
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xml.findingOccurrences = [{
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...xml.findingOccurrences[0],
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id: 'occurrence-xml',
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scanRunId: 'run-xml',
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importFileId: 'file-xml',
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sourceTableId: undefined,
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sourceIdentity: {
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tool: { id: 'pingcastle', name: 'PingCastle' },
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originalRuleId: 'S-Synthetic',
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canonicalRuleId: 'S-Synthetic',
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originalTitle: 'Synthetic finding'
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},
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originalFields: [
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{ key: 'risk-id', label: 'Rule ID', value: 'S-Synthetic' },
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{ key: 'title', label: 'Title', value: 'Synthetic finding' },
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{ key: 'points', label: 'Points', value: '20' }
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]
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}];
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const dataset = canonicalModelsToDataset([html, xml]);
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const xmlSheet = dataset?.sheets.find(sheet => sheet.sourceFile === 'ad_hc_synthetic.example.xml');
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const field = (label: string) => xmlSheet?.analysis.records[0].fields.find(item => item.label === label)?.value;
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expect(field('Points')).toBe('20');
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expect(field('Description')).toBeUndefined();
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expect(field('Technical explanation')).toBeUndefined();
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expect(field('Advised solution')).toBeUndefined();
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expect(field('Documentation')).toBeUndefined();
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expect(field('Rule Text Source')).toBeUndefined();
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expect(field('Verwandte Daten')).toBeUndefined();
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const xmlRecord = xmlSheet!.analysis.records[0];
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expect(linkPingCastleSectionSheets(xmlRecord, dataset!.sheets)).toEqual([]);
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const htmlSheet = dataset?.sheets.find(sheet => sheet.sourceFile === 'ad_hc_synthetic.example.html');
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const htmlRecord = htmlSheet!.analysis.records[0];
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expect(linkPingCastleSectionSheets(htmlRecord, dataset!.sheets)).toHaveLength(1);
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expect(xml.findingOccurrences[0].originalFields).toHaveLength(3);
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});
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it('keeps equal PingCastle section IDs isolated between import files', () => {
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const first = model();
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first.scanRuns = [{ ...first.scanRuns[0], tool: { id: 'pingcastle', name: 'PingCastle' } }];
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first.importFiles = [{
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...first.importFiles[0],
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fileName: 'ad_hc_synthetic.example.html',
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format: 'HTML',
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detectedTool: { id: 'pingcastle', name: 'PingCastle' }
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}];
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first.sourceTables = [];
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first.findingOccurrences = [{
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...first.findingOccurrences[0],
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sourceTableId: undefined,
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sourceIdentity: { tool: { id: 'pingcastle', name: 'PingCastle' }, originalRuleId: 'S-First', originalTitle: 'First synthetic finding' },
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originalFields: [
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{ key: 'risk-id', label: 'Rule ID', value: 'S-First' },
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{ key: 'title', label: 'Title', value: 'First synthetic finding' },
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{ key: 'details', label: 'Details', value: 'The detail can be found in Synthetic Section' },
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{
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key: 'linked-section',
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label: 'Verwandte Daten',
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value: {
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sections: [{
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sectionId: 'syntheticsection',
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sectionLabel: 'Synthetic Section',
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prose: '',
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headers: ['Name'],
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rows: [['Synthetic Entry']]
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}]
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}
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}
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]
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}];
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const second = model();
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second.assessment = { ...second.assessment, id: 'assessment-2' };
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second.scanRuns = [{ ...second.scanRuns[0], id: 'run-2', assessmentId: 'assessment-2', tool: { id: 'pingcastle', name: 'PingCastle' } }];
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second.importFiles = [{
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...second.importFiles[0],
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id: 'file-2',
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scanRunId: 'run-2',
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fileName: 'ad_hc_synthetic.example.html',
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format: 'HTML',
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detectedTool: { id: 'pingcastle', name: 'PingCastle' }
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}];
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second.sourceTables = [];
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second.findingOccurrences = [{
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...second.findingOccurrences[0],
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id: 'occurrence-2',
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scanRunId: 'run-2',
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importFileId: 'file-2',
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|
sourceTableId: undefined,
|
|
sourceIdentity: { tool: { id: 'pingcastle', name: 'PingCastle' }, originalRuleId: 'S-Second', originalTitle: 'Second synthetic finding' },
|
|
originalFields: [
|
|
{ key: 'risk-id', label: 'Rule ID', value: 'S-Second' },
|
|
{ key: 'title', label: 'Title', value: 'Second synthetic finding' },
|
|
{ key: 'details', label: 'Details', value: 'The detail can be found in Synthetic Section' },
|
|
{
|
|
key: 'linked-section',
|
|
label: 'Verwandte Daten',
|
|
value: {
|
|
sections: [{
|
|
sectionId: 'syntheticsection',
|
|
sectionLabel: 'Synthetic Section',
|
|
prose: '',
|
|
headers: ['Name'],
|
|
rows: [['Synthetic Entry']]
|
|
}]
|
|
}
|
|
}
|
|
]
|
|
}];
|
|
|
|
const dataset = canonicalModelsToDataset([first, second])!;
|
|
const expectedReferences = new Map([
|
|
['S-First', 'file-1::syntheticsection'],
|
|
['S-Second', 'file-2::syntheticsection']
|
|
]);
|
|
const sectionSheets = dataset.sheets.filter(sheet =>
|
|
[...expectedReferences.values()].map(pingCastleSectionSheetId).includes(sheet.id)
|
|
);
|
|
expect(sectionSheets).toHaveLength(2);
|
|
|
|
for (const [ruleId, referenceId] of expectedReferences) {
|
|
const sheet = dataset.sheets.find(item => item.analysis.records.some(record => record.fields.find(field => field.label === 'Rule ID')?.value === ruleId))!;
|
|
const record = sheet.analysis.records.find(item => item.fields.find(field => field.label === 'Rule ID')?.value === ruleId)!;
|
|
expect(pingCastleSectionIds(record)).toEqual([referenceId]);
|
|
expect(linkPingCastleSectionSheets(record, dataset.sheets).map(item => item.id)).toEqual([
|
|
pingCastleSectionSheetId(referenceId)
|
|
]);
|
|
expect(record.fields.find(field => field.label === 'Verwandte Daten')?.value).toBe(referenceId);
|
|
}
|
|
});
|
|
|
|
it('renders a prose-only section sheet when the resolved target table has no data rows', () => {
|
|
const stored = model();
|
|
stored.scanRuns = [{ ...stored.scanRuns[0], tool: { id: 'pingcastle', name: 'PingCastle' } }];
|
|
stored.importFiles = [{
|
|
...stored.importFiles[0],
|
|
fileName: 'ad_hc_synthetic.example.html',
|
|
format: 'HTML',
|
|
detectedTool: { id: 'pingcastle', name: 'PingCastle' }
|
|
}];
|
|
stored.sourceTables = [];
|
|
stored.findingOccurrences = [{
|
|
...stored.findingOccurrences[0],
|
|
sourceTableId: undefined,
|
|
sourceIdentity: { tool: { id: 'pingcastle', name: 'PingCastle' }, originalRuleId: 'S-EmptyTable', originalTitle: 'Synthetic empty-table finding' },
|
|
originalFields: [
|
|
{ key: 'risk-id', label: 'Rule ID', value: 'S-EmptyTable' },
|
|
{ key: 'title', label: 'Title', value: 'Synthetic empty-table finding' },
|
|
{
|
|
key: 'linked-section',
|
|
label: 'Verwandte Daten',
|
|
value: {
|
|
sections: [{
|
|
sectionId: 'emptysection',
|
|
sectionLabel: 'Empty Section',
|
|
prose: 'Synthetic explanation because no occurrences were found.',
|
|
headers: ['Policy Name', 'Setting', 'Value'],
|
|
rows: []
|
|
}]
|
|
}
|
|
}
|
|
]
|
|
}];
|
|
|
|
const dataset = canonicalModelToDataset(stored)!;
|
|
const sheet = dataset.sheets.find(item => item.id === pingCastleSectionSheetId('file-1::emptysection'))!;
|
|
expect(sheet.analysis.records).toHaveLength(1);
|
|
expect(sheet.analysis.records[0].fields.find(field => field.label === 'Inhalt')?.value)
|
|
.toBe('Synthetic explanation because no occurrences were found.');
|
|
expect(sheet.resultPreamble).toBeUndefined();
|
|
});
|
|
|
|
it('keeps the finding-specific PingCastle detail order and projects prose before table sheets', () => {
|
|
const stored = model();
|
|
stored.scanRuns = [{ ...stored.scanRuns[0], tool: { id: 'pingcastle', name: 'PingCastle' } }];
|
|
stored.importFiles = [{
|
|
...stored.importFiles[0],
|
|
fileName: 'ad_hc_synthetic.example.html',
|
|
format: 'HTML',
|
|
detectedTool: { id: 'pingcastle', name: 'PingCastle' }
|
|
}];
|
|
stored.sourceTables = [];
|
|
stored.findingOccurrences = [{
|
|
...stored.findingOccurrences[0],
|
|
sourceTableId: undefined,
|
|
sourceIdentity: { tool: { id: 'pingcastle', name: 'PingCastle' }, originalRuleId: 'S-Ordered', originalTitle: 'Ordered synthetic finding' },
|
|
originalFields: [{
|
|
key: 'linked-section',
|
|
label: 'Verwandte Daten',
|
|
value: {
|
|
sections: [
|
|
{ sectionId: 'overview', sectionLabel: 'Admin Groups', prose: 'Synthetic group explanation.', headers: ['Group'], rows: [['Administrators']] },
|
|
{ sectionId: 'members', sectionLabel: 'Admin Groups → Administrators', prose: 'Administrators Close', headers: ['Account'], rows: [['Synthetic Admin']] }
|
|
]
|
|
}
|
|
}]
|
|
}];
|
|
|
|
const dataset = canonicalModelToDataset(stored)!;
|
|
const findingSheet = dataset.sheets.find(item => item.sourceFile === 'ad_hc_synthetic.example.html')!;
|
|
const record = findingSheet.analysis.records[0];
|
|
const globallyReversed = [...dataset.sheets].reverse();
|
|
const linked = linkPingCastleSectionSheets(record, globallyReversed);
|
|
|
|
expect(linked.map(item => item.name)).toEqual([
|
|
'PingCastle · Admin Groups',
|
|
'PingCastle · Admin Groups → Administrators'
|
|
]);
|
|
expect(linked[0].resultPreamble).toBe('Synthetic group explanation.');
|
|
expect(linked[1].resultPreamble).toBeUndefined();
|
|
});
|
|
|
|
/**
|
|
* Regression: Blatt-IDs entstanden allein aus Dateiname und Blattname. Ein
|
|
* Bericht traegt dieselbe Tabelle aber mehrfach (PingCastle legt "Account
|
|
* list" je Regel erneut an), und derselbe Dateiname erscheint in mehreren
|
|
* Scanlaeufen. Die Blaetter bekamen dadurch dieselbe ID: React meldete
|
|
* doppelte Keys in Blattauswahl und Ergebnisfenster, und die Blattauswahl
|
|
* haette auf mehrere Blaetter zugleich gezeigt.
|
|
*/
|
|
it('vergibt eindeutige Blatt-IDs fuer gleichnamige Tabellen derselben Datei', () => {
|
|
const stored = model();
|
|
stored.sourceTables = [
|
|
{ id: 'table-a', importFileId: 'file-1', name: 'Account list', index: 1, rowCount: 1, columnCount: 2, attributes: {} },
|
|
{ id: 'table-b', importFileId: 'file-1', name: 'Account list', index: 2, rowCount: 1, columnCount: 2, attributes: {} }
|
|
];
|
|
stored.findingOccurrences = ['table-a', 'table-b'].map((tableId, index) => ({
|
|
...stored.findingOccurrences[0],
|
|
id: `occurrence-${tableId}`,
|
|
sourceTableId: tableId,
|
|
originalFields: [
|
|
{ key: 'rule', label: 'Rule ID', value: `PK-${index}`, columnIndex: 0 },
|
|
{ key: 'title', label: 'Title', value: `Finding ${index}`, columnIndex: 1 }
|
|
]
|
|
}));
|
|
|
|
const dataset = canonicalModelToDataset(stored)!;
|
|
const accountSheets = dataset.sheets.filter(sheet => sheet.sourceSheet === 'Account list');
|
|
|
|
expect(accountSheets).toHaveLength(2);
|
|
expect(new Set(accountSheets.map(sheet => sheet.id)).size).toBe(2);
|
|
expect(new Set(dataset.sheets.map(sheet => sheet.id)).size).toBe(dataset.sheets.length);
|
|
});
|
|
|
|
it('vergibt eindeutige Blatt-IDs fuer denselben Dateinamen in zwei Scanlaeufen', () => {
|
|
const first = model();
|
|
first.scanRuns = [{ ...first.scanRuns[0], tool: { id: 'pingcastle', name: 'PingCastle' } }];
|
|
first.importFiles = [{ ...first.importFiles[0], fileName: 'ad_hc_synthetic.example.html', format: 'HTML' }];
|
|
first.sourceTables = [];
|
|
first.findingOccurrences = [{ ...first.findingOccurrences[0], sourceTableId: undefined }];
|
|
|
|
const second = model();
|
|
second.assessment = { ...second.assessment, id: 'assessment-2' };
|
|
second.scanRuns = [{ ...first.scanRuns[0], id: 'run-2', assessmentId: 'assessment-2' }];
|
|
second.importFiles = [{ ...first.importFiles[0], id: 'file-2', scanRunId: 'run-2' }];
|
|
second.sourceTables = [];
|
|
second.findingOccurrences = [{
|
|
...first.findingOccurrences[0],
|
|
id: 'occurrence-2',
|
|
scanRunId: 'run-2',
|
|
importFileId: 'file-2',
|
|
sourceTableId: undefined
|
|
}];
|
|
|
|
const dataset = canonicalModelsToDataset([first, second])!;
|
|
const fileSheets = dataset.sheets.filter(sheet => sheet.sourceFile === 'ad_hc_synthetic.example.html');
|
|
|
|
expect(fileSheets).toHaveLength(2);
|
|
expect(fileSheets[0].name).toBe(fileSheets[1].name);
|
|
expect(new Set(dataset.sheets.map(sheet => sheet.id)).size).toBe(dataset.sheets.length);
|
|
});
|
|
|
|
it('returns null when the stored assessment has no findings', () => {
|
|
const empty = model();
|
|
empty.findingOccurrences = [];
|
|
expect(canonicalModelToDataset(empty)).toBeNull();
|
|
});
|
|
});
|