Module 6: Quiz

📌 Quiz Topic

Lesson 1: Defining Key Metrics and Designing Benchmarks

• Key Topics:

  1. Metrics for AI Code Quality:

     • Compilation success or test pass rates.

     • Estimated time savings or reduced coding effort.

     • Adoption rate: how much final code is AI-generated vs. manually written.

  2. Designing Benchmarks:

     • Choosing representative coding tasks or “challenges” (CRUD, algorithms, refactoring).

     • Setting up a “gold standard” repository with known solutions and test suites.

  3. Qualitative vs. Quantitative:

     • Style, readability, maintainability (qualitative).

     • Speed, correctness, coverage (quantitative).

Lesson 2: Running Comparisons Across Multiple AI Tools

• Key Topics:

  1. Selecting Tools:

     • Copilot, Cursor, Tabnine, CodeWhisperer, and others.

     • Criteria: language support, licensing, cost, ease of integration.

  2. Fair Testing Environment:

     • Same machine or environment variables, same version of the project.

     • Minimizing bias (e.g., not letting one tool “see” code generated by another).

  3. Collecting Data:

     • Time to complete a task.

     • Number of tries or corrections needed.

     • Overall pass/fail rate for tests.

Lesson 3: Interpreting Results and Communicating Findings

• Key Topics:

  1. Making Sense of Data:

     • Identifying patterns (e.g., if one tool consistently produces fewer bugs, but slightly slower coding).

     • Spotting outliers or anomalies (tools that fail trivial tests).

  2. Presenting to Stakeholders:

     • Summaries vs. detailed tables—know your audience.

     • Visual aids (bar charts for time, pass/fail rates, coverage).

  3. Strategic Decision-Making:

     • Balancing cost and performance.

     • Considering team preferences or existing tool ecosystems.

     • Potential next steps: a pilot program or partial rollout.

This module teaches you how to quantitatively and qualitatively evaluate AI code assistants and LLM-based tools. You’ll learn to design internal benchmarks, compare different solutions (e.g., Copilot vs. Cursor vs. Tabnine), and communicate results effectively within your organization.