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🚀 Quality Assurance Report for QSV's "describegpt" Command - QSV Version 0.109

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Introduction

👋 Welcome to the QA Report for the describegpt command in qsv v0.109. This report presents a comprehensive evaluation of the describegpt command's functionality. The command makes use ogf OpenAI's GPT chat completion models to infer extended metadata for CSV datasets, providing valuable insights into dataset descriptions, tags, and data dictionaries.

Test Plan

The QA testing for the describegpt command involved the following aspects:

  1. Integration Testing:

    • Confirm the successful integration of the "describegpt" command with OpenAI's GPT chat completion models API using the --openai-key option.
    • Evaluate the compatibility of different GPT models when specified via the --model option.

  2. Functional Testing:

    • Verify that each "describegpt" option (--all, --description, --dictionary, --tags) correctly produces the expected output and accurate metadata inferences.
    • Ensure that the command provides comprehensive metadata insights for different types of CSV files.

  3. Error Handling Testing:

    • Test the command's behavior when missing or invalid API keys are provided via the --openai-key option.
    • Validate the response when encountering invalid or unsupported GPT models specified using the --model option.

  4. Output Format Testing:

    • Verify that the --json option provides metadata results in valid JSON format.
    • Validate the --jsonl option for metadata output in JSON Lines format.

  5. Metadata Accuracy Testing:

    • Compare inferred metadata from "describegpt" with actual dataset characteristics to ensure accuracy and relevance.
    • Validate correctness of dataset descriptions, field types, labels, and statistics provided by --description and --dictionary.

  6. Option Combinations Testing:

    • Test various combinations of options to ensure seamless functioning and expected outputs.
    • Check for potential conflicts or unexpected behavior when multiple options are used together.

  7. Custom Prompt Testing:

    • Test the effectiveness of custom inferencing prompts provided via the --prompt-file option.
    • Assess how well the command adapts to various prompts and produces accurate metadata.

  8. Performance Testing:

    • Measure the processing time and resource utilization of the "describegpt" command with various dataset sizes.
    • Evaluate the effectiveness of the --timeout option in managing OpenAI completions.

  9. Default Option Behavior Testing:

    • Validate that the "describegpt" command behaves appropriately when no options are specified, and default behavior is followed.

Test Results

Head over to the Jupyter file for all the test results

Why Jupyter? Jupyter Notebook provides support for text editor, terminal commands and notebook, all in under one roof!

Types of CSV files used

To thoroughly test the describegpt command, we’ve used various types of CSV files that cover different scenarios and data structures.

  • Small CSV File: A simple CSV file with a few columns and rows, containing basic data types like integers, strings, and dates.

  • Medium sized CSV File: A basic CSV file with a few thousand columns and rows, containing data types like integers, strings, and dates.

  • Large CSV File: A CSV file with a large number of columns and rows, simulating real-world datasets with substantial data volume.

  • Empty CSV File: A CSV file with no records or data to test how the "describegpt" command handles empty datasets.

  • A corrupt CSV File: A CSV file that is damaged or contain errors. These errors can be caused by various issues, such as missing or mismatched delimiters, improper encoding, inconsistent quoting, or unexpected characters within the dataA CSV file with

  • CSV File with Missing Values: A CSV file containing missing or null values in some columns to evaluate how "describegpt" infe metadata in the presence of missing data.

  • CSV File with Special Characters: A CSV file containing special characters, symbols, and non-ASCII characters to test the command's ability to handle diverse data.

  • CSV File with Varying Data Types: A CSV file with columns containing different data types (e.g., numeric, string, date) to assess metadata inference accuracy.

  • CSV File with Long Texts: A CSV file with columns containing long text or free-form descriptions to verify if "describegpt" captures and summarizes the content effectively.

  • CSV File with Unstructured Data: A CSV file with unstructured data (e.g., multiple data formats in a single column) to test the command's handling of diverse data structures.

By using a diverse set of CSV files for testing, we can ensure that the describegpt command performs well in various scenarios and provides accurate and useful extended metadata for different types of datasets. This approach helps ensure that the command is robust and reliable for real-world use cases.

Documentation Review

The existing documentation for the "describegpt" command was reviewed for accuracy and completeness. The documentation effectively explains the feature's functionality, usage, and limitations.

Appendix

The Appendix contains detailed test case descriptions and steps for reference.

Recommendations

  • Provide additional documentation and examples on how to use the --prompt-file option effectively.
  • Consider exploring other GPT models to offer users a broader range of inferencing capabilities.

For any questions or feedback related to this QA report, please contact the QA team. The "describegpt" command options are ready for release, delivering enhanced metadata inference capabilities to QSV users.

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