This unit teaches students how to use generative AI and specialized tools to improve code quality, automate debugging, and produce developer documentation. Learners will practice test-driven workflows, evaluate multiple AI platforms and models, and apply automated refactoring and documentation techniques in small Python projects to prepare for capstone integration.
Learning Objectives
- Analyze AI-generated refactoring suggestions and select appropriate changes using code quality metrics, maintainability principles, and demonstrated unit test coverage
- Apply AI-assisted debugging and test-driven development workflows to create, run, and validate unit tests and automated bug fixes in a Python codebase
- Demonstrate the creation and evaluation of visual and API documentation produced with AI tools and platforms (e.g., OpenAI, Bard), producing at least one visual diagram and one API documentation artifact for a sample module
- Evaluate multiple generative AI models and developer platforms (e.g., AlphaCode, OpenAI, Google Bard, Dataset Search) for suitability, reliability, and ethical considerations when integrated into development workflows
Curriculum
- 1 Section
- 15 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 3 — AI-Assisted Code Quality and Tools15
- 1.1Generating Refactoring Suggestions20 mins
- 1.2Detecting Bugs with AI20 mins
- 1.3Test-Driven Development with AI20 mins
- 1.4Creating Visual Documentation20 mins
- 1.5Enhancing Code Quality with AI20 mins
- 1.6DeepMind AlphaCode20 mins
- 1.7Google’s Dataset Search20 mins
- 1.8Using AI as a Tutor20 mins
- 1.9Illuminating Generative AI Models20 mins
- 1.10Diving into the OpenAI Platform20 mins
- 1.11Tips and Tricks for Code Generation20 mins
- 1.12Automating Bug Fixes with AI20 mins
- 1.13Automating API Documentation with AI20 mins
- 1.14Google Bard20 mins
- 1.15edX20 mins
Instructor
Free
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