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added csv formulas (#108)
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‎README.md

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@@ -10,10 +10,10 @@ Our package bridges Cypress and Playwright test runs with Google Sheets or CSV,
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2. [Quick Start](#quick-start)
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- [Generate Reports in CSV Format](#generate-reports-in-csv-format)
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3. [Samples](#samples)
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- [Sheets Daily Report](#sheets-daily-report)
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- [Sheets Weekly Summary](#sheets-weekly-summary)
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- [Sheets Monthly Summary](#sheets-monthly-summary)
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- [CSV Daily Report](#csv-daily-report)
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- [Daily Report in Sheets](#daily-report-in-sheets)
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- [Weekly Summary in Sheets](#weekly-summary-in-sheets)
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- [Monthly Summary in Sheets](#monthly-summary-in-sheets)
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- [Daily Report in CSV](#daily-report-in-csv)
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4. [Sheets Setup Guide](#sheets-setup-guide)
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- [Cypress](#cypress)
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- [Playwright](#playwright)
@@ -87,25 +87,26 @@ Use either command to generate reports using either **NPX** or **NPM scripts** w
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## Samples
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### Sheets Daily Report
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### Daily Report in Sheets
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The Daily Summary features high-level metrics such as a test quantity and percentage pass rate across multiple categories. Test details are organized into columns that include: the feature area, spec title, test name, test type, test category, team identifiers, test priority, test status, test state, manual case id, error details, and execution speed in format mm:ss:mmm.
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The Daily Summary features high-level metrics such as a test quantity and percentage pass rate across multiple categories. Test details are organized into columns that include: the feature area, spec title, test name, test type, test category, team identifiers, test priority, test status, test state, manual case id, error details, and speed of execution (in mm:ss:mmm format).
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This structured format enables teams to quickly assess test outcomes on demand, with an example entry in the feature `area` '1-getting-started', in the 'todo' `spec`, the `test name` example to-do app can add new todo items', classified as `type` 'api', with no `category`, 'billing `team` is responsible, `status` 'passing' with a `speed` of '370 milliseconds'. The team may want to type in a `status` of 'billing team adding test category' with a `priority` of 'low'.
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![Screenshot of Feature](images/dailyReport.png)
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### Sheets Weekly Summary
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### Weekly Summary in Sheets
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The Weekly Summary is optional, and it provides a short term summary of the previous weeks `passed`, `failed`, `skipped`, and `total` test results. This summary is a great tool for teams who have weekly meetings to discuss their progress/status/gameplan
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![Screenshot of Feature](images/weeklySummary.png)
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### Sheets Monthly Summary
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### Monthly Summary in Sheets
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The Monthly Summary provides a brief overview of historical test execution results, giving long term visibility into `passed`, `failed`, `skipped`, and `total` test results for the previous month, allowing trends to emerge.
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![Screenshot of Feature](images/monthlySummary.png)
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### CSV Daily Report
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### Daily Report in CSV
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The Daily Report in CSV format delivers hardcoded snapshot of high-level test metrics, mirroring the formulaic insights provided by the Daily Summary in Sheets: feature area, spec title, test name, test type, test category, team identifiers, priority, status, state, manual case ID, error details, and speed of execution (in mm:ss:mmm format). Unlike the Sheets version, which leverages formulas for dynamic calculations, the CSV report contains hardcoded values, ensuring compatibility with static file formats while maintaining the same depth of analysis.
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![Screenshot of Feature](images/csvDailyReport.png)
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## Sheets Setup Guide

‎images/csvDailyReport.png

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‎package.json

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@@ -1,6 +1,6 @@
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{
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"name": "qa-shadow-report",
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"version": "2.1.1",
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"version": "2.1.2",
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"bin": {
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"qa-shadow-report": "./cli.js",
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"qasr": "./cli.js",

‎src/sharedMethods/csvHandler.js

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@@ -10,6 +10,9 @@ import chalk from 'chalk';
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* @returns {string} The CSV string.
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*/
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const arrayToCSV = (data) => {
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if (!Array.isArray(data) || !data.every((row) => Array.isArray(row))) {
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throw new Error('arrayToCSV expects a 2D array');
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}
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return data
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.map((row) =>
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row
@@ -50,5 +53,139 @@ export const saveCSV = (reportPayload, duplicate) => {
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}
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fs.writeFileSync(filePath, csvData, 'utf8');
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console.log(`CSV file has been saved successfully to ${filePath}`);
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console.info(
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chalk.green(`CSV file has been saved successfully to ${filePath}`)
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);
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};
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/**
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* Calculates daily summary metrics from test data, dynamically aggregating by unique TYPE, CATEGORY, TEAM values,
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* and aligns them in multiple rows. Ensures overall metrics are in columns 8-9. Handles empty/blank values efficiently.
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* @param {Array<Array<string>>} testData - Array of test data arrays (each with 12 elements).
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* @returns {string[][]} A 2D array representing the calculated header rows for CSV, aligned by columns.
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*/
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export const calculateDailySummaryMetrics = (testData) => {
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// Initialize counters
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const counters = {
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total: testData.length,
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passed: 0,
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failed: 0,
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skippedPending: 0,
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byType: {},
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byCategory: {},
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byTeam: {},
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};
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// Helper to update counters for a given key and map
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const updateCounter = (key, map, status) => {
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if (!key) return; // Skip empty/blank keys
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if (!map[key]) map[key] = { passed: 0, total: 0 };
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map[key].total++;
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if (status.toLowerCase() === 'passed') map[key].passed++;
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};
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// Process each test entry
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testData.forEach((row, index) => {
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const type = row[3] || '';
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const category = row[4] || '';
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const team = row[5] || '';
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const status = row[8] || '';
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// Update overall counters
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if (status.toLowerCase() === 'passed') counters.passed++;
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if (status.toLowerCase() === 'failed') counters.failed++;
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if (status.toLowerCase() !== 'passed' && status.toLowerCase() !== 'failed')
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counters.skippedPending++;
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// Aggregate by TYPE, CATEGORY, TEAM
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updateCounter(type, counters.byType, status);
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updateCounter(category, counters.byCategory, status);
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updateCounter(team, counters.byTeam, status);
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});
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// Calculate percentages
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const calculatePercentage = (passed, total) =>
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total > 0 ? Math.round((passed / total) * 100) : 0;
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// Helper to generate metric value
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const generateMetricValue = (passed, total) =>
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`${passed} of ${total} - (${calculatePercentage(passed, total)}%)`;
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// Get metrics for each category
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const typeMetrics = Object.entries(counters.byType).map(
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([key, { passed, total }]) => [
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`# ${key} tests passed`,
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generateMetricValue(passed, total),
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]
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);
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const categoryMetrics = Object.entries(counters.byCategory).map(
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([key, { passed, total }]) => [
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`# ${key} tests passed`,
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generateMetricValue(passed, total),
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]
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);
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const teamMetrics = Object.entries(counters.byTeam).map(
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([key, { passed, total }]) => [
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`# ${key} tests passed`,
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generateMetricValue(passed, total),
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]
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);
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const overallMetrics = [
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`# passed tests`,
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`${counters.passed} (${calculatePercentage(counters.passed, counters.total)}%)`,
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`# failed tests`,
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`${counters.failed} (${calculatePercentage(counters.failed, counters.total)}%)`,
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`# skipped/pending tests`,
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`${counters.skippedPending} (${calculatePercentage(counters.skippedPending, counters.total)}%)`,
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`# total tests`,
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`${counters.total}`,
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];
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// Determine the number of rows (at least 4 for overall metrics)
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const maxUniqueMetrics = Math.max(
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typeMetrics.length,
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categoryMetrics.length,
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teamMetrics.length
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);
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const minRows = 4; // Ensure at least 4 rows for overall metrics
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const maxRows = Math.max(maxUniqueMetrics, minRows);
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// Build the 2D header with dynamically determined rows
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const headerRows = [
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// Initial empty row
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Array(12).fill(''),
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];
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for (let i = 0; i < maxRows; i++) {
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const row = [];
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// Columns 1-2: TYPE metric (if available)
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row.push(typeMetrics[i]?.[0] || '', typeMetrics[i]?.[1] || '');
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// Columns 3-4: CATEGORY metric (if available)
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row.push(categoryMetrics[i]?.[0] || '', categoryMetrics[i]?.[1] || '');
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// Columns 5-6: TEAM metric (if available)
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row.push(teamMetrics[i]?.[0] || '', teamMetrics[i]?.[1] || '');
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// Column 7: Placeholder
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row.push('');
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// Columns 8-9: Overall metrics (fixed to first 4 rows)
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if (i === 0) {
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row.push(overallMetrics[0] || '', overallMetrics[1] || '');
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} else if (i === 1) {
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row.push(overallMetrics[2] || '', overallMetrics[3] || '');
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} else if (i === 2) {
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row.push(overallMetrics[4] || '', overallMetrics[5] || '');
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} else if (i === 3) {
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row.push(overallMetrics[6] || '', overallMetrics[7] || '');
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} else {
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row.push('', '');
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}
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// Columns 10-12: Placeholders
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row.push('', '', '');
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headerRows.push(row);
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}
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// Add final empty row
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headerRows.push(Array(12).fill(''));
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return headerRows;
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};

‎src/sharedMethods/dailyReportHandler.js

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@@ -19,7 +19,7 @@ import {
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setTextWrappingToClip,
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} from './styles.js';
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import { TEST_DATA } from '../../constants.js';
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import { saveCSV } from './csvHandler.js';
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import { calculateDailySummaryMetrics, saveCSV } from './csvHandler.js';
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import { doesTodaysReportExist } from './dailyReportRequired.js';
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import { transformPlaywrightToFriendlyFormat } from './convertPayloads.js';
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import chalk from 'chalk';
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}
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const fullDailyPayload = await buildDailyPayload(testPayload, playwright);
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if (csv) {
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const reportPayload = [
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const summaryHeader = calculateDailySummaryMetrics(
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fullDailyPayload.bodyPayload
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);
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// Ensure the original header is a 1D array
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const originalHeader = Array.isArray(
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fullDailyPayload.headerPayload[
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fullDailyPayload.headerPayload.length - 1
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],
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...fullDailyPayload.bodyPayload,
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]
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)
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? fullDailyPayload.headerPayload[
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fullDailyPayload.headerPayload.length - 1
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]
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: [
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fullDailyPayload.headerPayload[
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fullDailyPayload.headerPayload.length - 1
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],
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];
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// Pad the original header to match body row length (12 columns)
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const bodyRowLength = fullDailyPayload.bodyPayload[0]?.length || 12;
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const paddedOriginalHeader = [
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...originalHeader,
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...Array(Math.max(0, bodyRowLength - originalHeader.length)).fill(''),
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];
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const reportPayload = [
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...summaryHeader, // Multi-row header, each row is 12 columns
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paddedOriginalHeader, // Column names, padded to 12 columns
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...fullDailyPayload.bodyPayload, // Already 2D, each row is 12 columns
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];
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saveCSV(reportPayload, duplicate);
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} else {

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