Developed QA Testing Workflow for NoSQL Data

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Developed QA Testing Workflow for NoSQL Data

  • Day: 2024-09-17
  • Time: 13:30 to 14:10
  • Project: Business
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: QA, Nosql, Python, AI, Data Validation

Description

Session Goal

The session aimed to develop and refine a quality assurance (QA) testing workflow for NoSQL data, leveraging automation and AI-driven processes.

Key Activities

  • Prepared a memo for the lead developer and project manager to finalize key points and assign QA tasks based on 50 parsed results.
  • Summarized progress on schema parsing and data processing, outlining challenges and next steps for QA tasks involving AI agents.
  • Outlined the structure for a Jupyter notebook to perform QA testing on extracted NoSQL data, focusing on field validation and discrepancy identification.
  • Developed a Python workflow for QA testing of NoSQL data, emphasizing loading existing data and processing new resolutions.
  • Created a Python function to compare parsed data fields with original text, specifically checking references and licitations.
  • Provided a dynamic Python function for comprehensive QA comparison, handling lists, dictionaries, and simple values.
  • Updated an AI-driven workflow for comparing PDF text with parsed NoSQL data, using OpenAI for feedback and improvement suggestions.
  • Refined an AI prompt for legal document QA, focusing on necessary modifications and discrepancies.
  • Implemented a Python code snippet for iterative file saving of QA results, ensuring data is appended with error handling and logging.

Achievements

  • Successfully developed a comprehensive QA testing workflow for NoSQL data, integrating Python and AI tools.
  • Finalized key points and feedback mechanisms for the development team.

Pending Tasks

  • Further refinement of AI-driven QA processes and integration with existing systems.
  • Continuous monitoring and improvement of the QA testing workflow based on feedback and results.

Evidence

  • source_file=2024-09-17.sessions.jsonl, line_number=1, event_count=0, session_id=83798282262b9b3d90f3a17bdcc6e66b1124be446d321c0811c28c8543d8e156
  • event_ids: []

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