Hello and welcome to this lesson which is going to focus on refactoring strategy at the architectural level. Specifically, we will focus on planning, risk assessment, and control of change rather than mechanical code transformations. We will examine how AI assistance fits into enterprise refactoring decisions while maintaining human judgement for business-critical changes.

Intended Audience:

This lesson has been designed for budding software architects and developers who are new to AI tooling generally, or those who have some experience but are looking to expand their knowledge. This course leads onto some Instructor Led Training for Building Software using AI tools.

Learning Objectives:

By the end of this lesson, you will have a greater understanding of:

The framework for safe, high-value refactoring, including the four-part structure: AI assistance, performance thinking, planning, and regression strategy.

The critical distinction between mechanical and semantic refactoring. This includes understanding that AI tools are brilliant at mechanical changes (like renaming or extracting), but human judgement is non-negotiable for semantic changes that clarify system behavior or domain rules.

How AI assistance can be strategically applied to planning. This includes using AI to propose candidate seams, cluster change reasons into coherent scopes, and restate acceptance criteria as invariants.
Advanced planning concepts for mitigating risk and managing failure. This involves performing risk assessment through blast-radius mapping, and establishing failure planning measures such as defining stop rules, rollback pre-conditions, and rehearsing playbooks.

Prerequisites:

This course is part of an introduction to AI tooling, and we will expand your knowledge from a beginner position. However, knowledge of any AI tools would put you at an advantage.

Feedback:

Feedback on our courses here at QA is valuable to both us as trainers and any students looking to take the same course in the future. If you have any feedback, positive or negative, it would be greatly appreciated if you could send an email to platformsupport@qa.com.

Please note that, at the time of writing this content, all course information was accurate. AI tools evolve rapidly and as a result, minor discrepancies may appear in the course content over time. Here at QA, we strive to keep our content up to date in order to provide the best training available.

So, if you notice any information that is outdated, please contact us on the same address (platformsupport@qa.com). This will allow us to update the course during its next release cycle.

Refactoring for Readability and Performance