This unit explores practical workflows for using AI tools to generate, improve, test, and maintain software. Students practice applying code-generation, refactoring, linting, and testing techniques while learning about transformer-based models and tools such as Copilot, ChatGPT, Sourcegraph Cody, Kaggle, and Replit.
Learning Objectives
- Analyze the strengths, limitations, and appropriate use cases of AI code assistants and transformer models for generating and reviewing code
- Apply AI-driven workflows to generate code from a software requirements specification (SRS), produce comments/annotations, and integrate AI output with manually written code
- Demonstrate the use of linting tools and testing frameworks, including AI-assisted linting and test generation, to prevent, detect, and fix bugs
- Evaluate strategies for refactoring, maintaining, and optimizing code using AI tools and platform-specific features (e.g., Sourcegraph Cody, Replit, Kaggle) while addressing reliability and maintainability
Curriculum
- 1 Section
- 15 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 2 — AI-Assisted Coding and Maintenance15
- 1.1Chatting with Copilot20 mins
- 1.2Generating Code from an SRS20 mins
- 1.3Refactoring with AI20 mins
- 1.4Preventing Bugs with Linting20 mins
- 1.5Optimizing Your Code with AI20 mins
- 1.6Working with a Testing Framework20 mins
- 1.7Generating Code Comments and Annotations20 mins
- 1.8Utilizing AI for Code Maintenance20 mins
- 1.9Sourcegraph Cody20 mins
- 1.10Kaggle20 mins
- 1.11Linting with AI20 mins
- 1.12Understanding Transformers20 mins
- 1.13Reviewing Replit20 mins
- 1.14Chatting with ChatGPT20 mins
- 1.15Blending Manually Written and AI-Generated Code20 mins
Instructor
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