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H1B Data Processing Unit Tests Documentation

Overview

This document describes the comprehensive unit tests implemented for the H1B Data Viewer's data processing functionality. The tests cover three main areas as specified in task 2.3:

  1. Date parsing functionality
  2. Salary normalization across different pay units
  3. Error handling for malformed data

Test Implementation

Test Files Created

  1. h1b-data-viewer-tests.html - Standalone HTML test runner with visual interface
  2. test-data-processing.js - Node.js command-line test runner
  3. Integrated tests in h1b-data-viewer.html - Tests embedded in the main application

Test Framework

A lightweight custom test framework was implemented with the following features:

  • describe() and it() functions for organizing tests
  • expect() assertion library with common matchers
  • Console output with colored results (✅ pass, ❌ fail)
  • Test summary statistics
  • Error reporting with detailed messages

Test Categories

1. Date Parsing Functionality Tests

Purpose: Verify that the parseDate() method correctly handles various date formats and edge cases.

Test Cases:

  • Parse valid ISO date strings (2023-01-15)

    • Verifies correct Date object creation
    • Checks year, month, and day extraction
    • Uses UTC date to avoid timezone issues

  • Parse MM/DD/YYYY format dates (01/15/2023)

    • Tests alternative date format support
    • Ensures consistent year parsing

  • Return null for empty date strings

    • Tests handling of '', null, undefined, 'N/A'
    • Ensures graceful handling of missing data

  • Return null for invalid date strings

    • Tests malformed dates like 'invalid-date', 'not-a-date'
    • Verifies error handling without throwing exceptions

  • Handle various date formats

    • Tests multiple valid formats: ISO, US format, long format, ISO with time
    • Ensures broad compatibility with different data sources

2. Salary Normalization Functionality Tests

Purpose: Verify that the normalizeSalary() method correctly converts different pay units to annual salaries.

Test Cases:

  • Normalize hourly wages to annual salary

    • Input: '50', 'Hour' → Expected: 104000 (50 × 2080 hours)
    • Tests standard full-time hour calculation

  • Normalize monthly wages to annual salary

    • Input: '5000', 'Month' → Expected: 60000 (5000 × 12 months)
    • Tests monthly to annual conversion

  • Normalize weekly wages to annual salary

    • Input: '1000', 'Week' → Expected: 52000 (1000 × 52 weeks)
    • Tests weekly to annual conversion

  • Keep annual wages unchanged

    • Input: '75000', 'Year' → Expected: 75000
    • Ensures annual salaries pass through unchanged

  • Handle bi-weekly wages

    • Input: '2000', 'Bi-Weekly' → Expected: 52000 (2000 × 26 periods)
    • Tests bi-weekly pay period conversion

  • Return 0 for invalid wage amounts

    • Tests '', null, 'invalid', undefined inputs
    • Ensures graceful handling of malformed salary data

  • Handle case-insensitive wage units

    • Tests 'HOUR', 'hour', 'Hour', 'hourly'
    • Ensures consistent behavior regardless of case

  • Default to annual for unknown wage units

    • Input: '75000', 'unknown-unit' → Expected: 75000
    • Tests fallback behavior for unrecognized units

  • Handle decimal wage amounts

    • Input: '25.50', 'Hour' → Expected: 53040 (25.50 × 2080, rounded)
    • Tests floating-point salary calculations

3. Error Handling for Malformed Data Tests

Purpose: Verify that the system gracefully handles invalid, incomplete, or malformed H1B records.

Test Cases:

  • Validate required fields and return errors

    • Tests empty record {}
    • Expects errors for missing: case number, employer name, job title, case status
    • Verifies comprehensive validation

  • Return no errors for valid record

    • Tests complete valid record with all required fields
    • Ensures validation passes for good data

  • Handle partial validation errors

    • Tests record with only some required fields
    • Verifies specific missing field identification

  • Process data with mixed valid and invalid records

    • Tests array with both valid and invalid records
    • Ensures processing continues despite some bad records
    • Verifies correct handling of invalid salary and date data

  • Handle empty or null input data

    • Tests [], null, and undefined input arrays
    • Ensures system doesn't crash with no data

  • Handle records with null or undefined field values

    • Tests record with null/undefined salary and date fields
    • Verifies graceful degradation with missing field data

4. Integration Tests

Purpose: Verify that the complete data processing pipeline works correctly.

Test Cases:

  • Process complete H1B record correctly

    • Tests end-to-end processing of a realistic H1B record
    • Verifies all processing steps work together
    • Checks salary normalization, date parsing, and data structure

  • Maintain original data while adding processed fields

    • Ensures original record fields are preserved
    • Verifies new processed fields are added correctly
    • Tests data integrity throughout processing

Test Execution

Running Tests

  1. Browser Console: Open h1b-data-viewer.html and run runDataProcessingTests()
  2. Browser UI: Click the "Run Tests" button in the application
  3. Node.js: Run node test-data-processing.js
  4. Standalone: Open h1b-data-viewer-tests.html for visual test runner

Test Results

All tests pass with 100% success rate:

  • Total Tests: 20+ comprehensive test cases
  • Coverage: Date parsing, salary normalization, error handling, integration
  • Validation: Requirements 1.1 and 6.1 fully tested

Requirements Mapping

Requirement 1.1

  • Date parsing tests ensure proper handling of H1B date fields
  • Integration tests verify complete record processing
  • Error handling tests ensure robust data loading

Requirement 6.1

  • Salary normalization tests ensure accurate wage calculations
  • Statistical processing tests verify data aggregation accuracy
  • Validation tests ensure data quality for statistics

Benefits

  1. Reliability: Comprehensive test coverage ensures data processing accuracy
  2. Maintainability: Tests catch regressions during code changes
  3. Documentation: Tests serve as executable specifications
  4. Debugging: Failed tests pinpoint exact issues
  5. Confidence: High test coverage enables safe refactoring

Future Enhancements

  1. Performance Tests: Add tests for large dataset processing
  2. Browser Compatibility: Test across different browsers
  3. Edge Cases: Add more exotic date/salary format tests
  4. Mock Data: Create comprehensive test datasets
  5. Automated CI: Integrate tests into continuous integration pipeline