AI-Assisted Coding, Testing, and Debugging for Developers

Accelerate everyday development with AI

Generative AI can speed up everyday development tasks, but effective use depends on knowing what to automate, what to verify, and when developer judgement matters most.

On this course, you’ll apply AI directly to coding, testing, debugging, and documentation while keeping quality firmly in focus.

You’ll move from prompting AI for individual code snippets to using it as part of a more structured build-and-test workflow.

Throughout the course, you’ll practise reviewing outputs critically rather than treating AI-generated code as a finished solution.

Generate and refine code with AI

Use natural language and technical context to generate functions, components, and boilerplate before applying AI-assisted refactoring to improve readability, performance, and adherence to best practices.

You’ll explore how clearer instructions and stronger context can produce more useful code suggestions.

Strengthen testing and troubleshoot faster

Apply AI to unit test generation, edge cases, and test coverage, then use it to interpret errors, investigate likely causes, and suggest potential fixes.

You’ll learn to validate recommendations and distinguish useful debugging support from unreliable output.

Keep code and documentation in sync

Use generative AI to support code comments, READMEs, and technical explanations while maintaining accuracy and consistency as software changes.

You’ll bring these techniques together in an AI-assisted development workflow that keeps the developer in control.

By the end, you’ll be able to use generative AI more effectively across coding, testing, debugging, and documentation while maintaining the standards expected in real software development.

This course is designed for software engineers, full stack and application developers, and QA-minded developers who want to use AI across coding, testing, and debugging. Programming skills and familiarity with generative AI and prompting are required.

Learners need a computer and a stable internet connection to follow the course demonstrations and explore AI-assisted development workflows. The course introduces tools such as GitHub Copilot, Tabnine, Refact, Amazon Q, Gemini, Qodo Gen, Replit, Cursor, Windsurf, and Trae AI Builder. Specific operating system requirements, installation requirements, account requirements, and licensing details are not fully specified in the course material and should be confirmed before finalising the setup instructions.

This course is designed for software engineers, full stack and application developers, and QA-minded developers who want to use AI across coding, testing, and debugging. Programming skills and familiarity with generative AI and prompting are required.

• Apply AI-assisted coding tools to generate working code from natural-language requirements and project context.
• Evaluate AI-generated code for readability, performance, maintainability, and behaviour preservation through refactoring and review.
• Create unit tests and edge cases with AI assistance to improve code coverage and software quality.
• Develop candidate fixes by analysing errors and diagnosing likely causes through structured AI-assisted debugging approaches.
• Assess AI-assisted build-and-test workflows through testing, verification, documentation, security checks, and developer oversight.