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I know I know, using ChatGPT is cheating. Or is it working smarter? In any case, it seems right at first glance, and ChatGPT has helped identify a number of issues I may have missed (I did see some of them at first glance, but not all).

Issues in the CSV Data

  1. Invalid Date Format: The invoice date for the second record is 40/04/2024, which is an invalid date as there is no 40th day in any month.
  2. Missing Company Name: The fourth record is missing a company name, which could potentially cause the service to fail if this field is required.
  3. Misplaced Field: The fifth record appears to have fields out of order. The Rental Agreement Number is missing, and the Account Number 3333335 seems to be in its place.
  4. Alphanumeric Account Number: The last record contains an alphanumeric account number 333333X, which may not be acceptable if the service expects numeric values only.
  5. Trailing Commas: The fourth and fifth records have trailing commas, suggesting there might be expected fields that are missing.
  6. Inconsistent Date and Time Formats: While the Invoice Date uses a dd/mm/yyyy format, the Check Out Date and Check In Date are in an ISO format with timestamps. This inconsistency might cause issues if not handled properly.

Test Cases for CSV Processing Service

Validity Tests

  • Test Case 1: Input a CSV with valid data to verify that the service processes it without errors.
  • Test Case 2: Input a CSV with an invalid date format like 40/04/2024 to check if the service can handle date validation.
  • Test Case 3: Input a CSV where mandatory fields like Company Name are missing to test the service's handling of incomplete data.

Format Consistency Tests

  • Test Case 4: Input a CSV with mixed date formats to ensure the service can normalize or reject inconsistent formats.
  • Test Case 5: Input a CSV where fields are out of order to check if the service can correctly map fields based on headers rather than sequence.

Robustness Tests

  • Test Case 6: Input a CSV with alphanumeric characters in fields expected to be numeric to test how the service handles type mismatches.
  • Test Case 7: Test with a CSV that has extra commas or unexpected additional fields to see if the service can ignore or appropriately handle extra data.
  • Test Case 8: Input a CSV with completely empty lines or records to verify how the service handles empty inputs.

Stress Tests

  • Test Case 9: Input a very large CSV file to test the service's performance under load.
  • Test Case 10: Input a CSV with rapidly changing data types across records to assess the flexibility and adaptability of the parsing logic.

These test cases can help ensure that the CSV processing service is robust, flexible, and can handle a variety of real-world data scenarios.