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StephenZhaoyi / LLM-Course-Check

Use LLM to do LLM's course Check for Technical University of Munich
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complete the non functional requirements #6

Closed StephenZhaoyi closed 2 weeks ago

8arry commented 2 weeks ago

Non-Functional Requirements.md

8arry commented 2 weeks ago

Non-Functional Requirements

  1. Usability

    • The system should provide a user-friendly interface for the selection committee, making it easy to navigate and use without extensive training.
    • Clear visualization of recommendations should be included to aid decision-making.

  2. Performance

    • The LLM-based evaluation system should process and analyze application materials within a reasonable timeframe (under 10 seconds per application) to ensure timely evaluation during peak application periods.
    • The system must handle concurrent requests from multiple users (at least 20 simultaneous users) without performance degradation.

  3. Reliability

    • The system should have an uptime of at least 99.5% to ensure availability during critical application evaluation periods.
    • The evaluation algorithms should consistently provide accurate recommendations with minimal errors in scoring and ranking.

  4. Scalability

    • The system should be scalable to accommodate an increase in the number of applications, especially during peak admission cycles.
    • The backend infrastructure should support scaling to analyze data from diverse programs and courses without requiring substantial reconfiguration.

  5. Security

    • The system must ensure the confidentiality of applicant data by implementing robust encryption methods for data storage and transmission.
    • Only authorized personnel should have access to the application data, with user authentication and role-based access controls enforced.

  6. Fairness and Transparency

    • Evaluation algorithms must be fair and unbiased, ensuring no discrimination based on any personal or demographic attributes.
    • The scoring methodology and evaluation process should be transparent, with explanations available for each recommendation.

  7. Maintainability

    • The codebase should be modular and well-documented to facilitate easy updates and maintenance by future developers.
    • System components should be designed to allow for easy modification of evaluation criteria as the university’s admission policies evolve.

  8. Compliance

    • The system should comply with relevant data protection regulations, such as GDPR, ensuring that applicants’ personal data is handled responsibly.
    • The evaluation and recommendation criteria should align with the Technical University of Munich’s admission standards and policies.