Effective Work Samples for Evaluating AI-Assisted Code Refactoring Skills

AI-assisted code refactoring represents a significant evolution in software development practices. As AI coding assistants like GitHub Copilot, Amazon CodeWhisperer, and ChatGPT become increasingly sophisticated, the ability to effectively leverage these tools for code improvement has become a valuable skill in modern development teams. Developers who excel at AI-assisted refactoring can dramatically increase productivity, improve code quality, and reduce technical debt.

However, identifying candidates who truly understand how to work effectively with AI coding tools requires more than just reviewing resumes or conducting traditional interviews. The nuanced skills involved—balancing AI suggestions with human judgment, understanding refactoring principles, and maintaining code quality—are best evaluated through practical demonstrations.

Work samples provide a window into how candidates approach real-world refactoring scenarios with AI assistance. They reveal not just technical competence but also critical thinking skills, as effective AI-assisted refactoring requires developers to evaluate, modify, and sometimes override AI suggestions. The best practitioners know when to trust AI recommendations and when human expertise should take precedence.

The following work samples are designed to assess candidates' abilities across the full spectrum of AI-assisted code refactoring. From planning complex refactoring projects to hands-on implementation with AI tools, these exercises will help you identify candidates who can truly leverage AI to transform and improve your codebase while maintaining high standards of code quality and performance.

Activity #1: Legacy Code Transformation Challenge

This activity evaluates a candidate's ability to plan and execute a comprehensive refactoring strategy using AI assistance. It tests their understanding of code smells, refactoring patterns, and their skill in directing AI tools to assist with complex transformations. Candidates must demonstrate both strategic thinking and tactical implementation skills while leveraging AI effectively.

Directions for the Company:

• Select a 200-300 line segment of legacy code from your actual codebase (with sensitive information removed) that contains multiple code smells such as long methods, duplicate code, or complex conditional logic.
• Provide access to an AI coding assistant (GitHub Copilot, Amazon CodeWhisperer, or a ChatGPT interface with code capabilities).
• Allow candidates to use their preferred IDE with the AI tool integrated.
• Allocate 45-60 minutes for this exercise.
• Prepare a document outlining the business context of the code and any specific performance or maintainability concerns.

Directions for the Candidate:

• Review the provided legacy code and identify 3-5 key areas that would benefit from refactoring.
• Create a brief refactoring plan document (5-10 minutes) outlining your approach and priorities.
• Use the provided AI coding assistant to help refactor the code, focusing on improving readability, maintainability, and performance.
• Document instances where you accepted, modified, or rejected AI suggestions and explain your reasoning.
• Be prepared to explain how your refactored solution improves upon the original code.

Feedback Mechanism:

• After the candidate completes the refactoring, provide feedback on one aspect they handled well (e.g., "Your extraction of the validation logic into a separate function improved readability").
• Offer one specific improvement suggestion (e.g., "The AI suggested using a design pattern here that you didn't implement, which might have further simplified the code").
• Give the candidate 10 minutes to implement the suggested improvement using the AI assistant, observing how they incorporate feedback and direct the AI tool.

Activity #2: AI Prompt Engineering for Code Optimization

This activity focuses on the candidate's ability to effectively communicate with AI tools through well-crafted prompts. It tests their understanding of how to guide AI systems to produce optimal code suggestions for specific refactoring needs, an essential skill for maximizing the value of AI coding assistants.

Directions for the Company:

• Prepare a code snippet (50-100 lines) with clear performance bottlenecks or optimization opportunities.
• Provide access to a text-based AI coding assistant like ChatGPT or Claude that responds to prompts rather than inline suggestions.
• Create a document outlining specific performance requirements or constraints (e.g., memory limitations, response time targets).
• Allocate 30-45 minutes for this exercise.
• Have a developer familiar with the code available to answer clarifying questions.

Directions for the Candidate:

• Analyze the provided code and identify optimization opportunities.
• Craft a series of prompts for the AI assistant aimed at generating optimized code alternatives.
• Document your prompt strategy, explaining how each prompt is designed to elicit specific types of optimization suggestions.
• Evaluate the AI's responses, selecting and potentially combining the most effective suggestions.
• Implement the optimized solution, documenting which parts came from AI suggestions and which required your modifications.
• Be prepared to explain why certain AI suggestions were more helpful than others.

Feedback Mechanism:

• Provide feedback on the effectiveness of the candidate's prompting strategy (e.g., "Your incremental approach to refining prompts yielded increasingly better suggestions").
• Suggest one way the candidate could improve their prompt engineering (e.g., "Including more context about the performance constraints might have led to more targeted optimization suggestions").
• Allow the candidate 5-10 minutes to craft an improved prompt based on your feedback and discuss how the new AI suggestions differ from the original ones.

Activity #3: Critical Evaluation of AI Refactoring Suggestions

This exercise tests a candidate's ability to critically evaluate AI-generated code suggestions, identifying potential issues that AI might miss. It assesses their understanding of security implications, edge cases, and maintainability concerns that require human oversight during AI-assisted refactoring.

Directions for the Company:

• Prepare a code sample (100-150 lines) with subtle issues such as potential security vulnerabilities, edge cases, or maintainability concerns.
• Generate 5-7 AI-suggested refactoring changes for this code, ensuring some suggestions contain subtle problems (e.g., introducing security vulnerabilities, ignoring edge cases, or violating team coding standards).
• Create a document with the original code and the AI-suggested changes clearly marked.
• Allocate 30-40 minutes for this exercise.
• Provide access to any relevant coding standards or security guidelines your team follows.

Directions for the Candidate:

• Review each AI-suggested refactoring change carefully.
• For each suggestion, provide a written assessment indicating whether you would:
• Accept the suggestion as-is
• Modify the suggestion (explaining how)
• Reject the suggestion entirely (explaining why)
• Identify any security vulnerabilities, performance issues, or maintainability concerns introduced by the AI suggestions.
• Propose alternative approaches for the problematic suggestions.
• Prioritize which refactoring changes would provide the most value if implemented.

Feedback Mechanism:

• Highlight one instance where the candidate showed particularly good judgment in evaluating an AI suggestion (e.g., "You correctly identified the potential SQL injection vulnerability that the AI introduced").
• Point out one evaluation where the candidate might have missed an important consideration (e.g., "This suggestion actually introduces a subtle race condition that wasn't addressed in your evaluation").
• Ask the candidate to revise their assessment of the highlighted suggestion, observing how they incorporate the new insight.

Activity #4: Collaborative Refactoring Planning Session

This activity evaluates a candidate's ability to plan a large-scale refactoring project that would leverage AI assistance. It tests their strategic thinking, communication skills, and understanding of how to effectively integrate AI tools into a team's development workflow.

Directions for the Company:

• Prepare a description of a fictional but realistic large-scale refactoring project (e.g., migrating from a monolith to microservices, updating a legacy codebase to modern standards, or implementing a new architecture pattern).
• Include high-level metrics about the codebase (size, languages used, current pain points).
• Create a list of available AI tools and their capabilities that could be used for the project.
• Allocate 45-60 minutes for this exercise.
• Have 1-2 team members participate in the session as collaborators.

Directions for the Candidate:

• Review the project description and prepare a 15-minute presentation outlining:
• Your approach to breaking down the refactoring project into manageable phases
• How you would leverage AI tools at each phase
• Potential risks and how to mitigate them
• How you would measure success
• Include specific examples of where AI assistance would be most valuable and where human oversight would be critical.
• Collaborate with the team members to refine the approach, answering questions and incorporating their feedback.
• Create a one-page summary document of the final approach that could be shared with stakeholders.

Feedback Mechanism:

• Provide feedback on one particularly strong aspect of the candidate's planning approach (e.g., "Your phased approach to refactoring with clear checkpoints would minimize risk").
• Suggest one area where the plan could be improved (e.g., "The plan could benefit from more specific criteria for when to trust AI suggestions versus when to require peer review").
• Give the candidate 10 minutes to revise the relevant section of their plan, observing how they incorporate the feedback and adapt their thinking.

Frequently Asked Questions

How should we select the code samples for these exercises?

Choose code that represents real challenges in your codebase but doesn't require extensive domain knowledge to understand. The best samples contain common code smells and opportunities for improvement that are recognizable to experienced developers. When possible, use anonymized versions of actual code from your projects rather than contrived examples.

What if candidates don't have experience with our specific AI coding tools?

Focus on evaluating the candidate's approach to working with AI tools rather than their familiarity with specific platforms. Most AI-assisted refactoring skills transfer well between tools. Consider allowing a brief familiarization period (15-20 minutes) before the timed exercise if you're using a specialized tool. Alternatively, let candidates use an AI tool they're familiar with if the core skills being tested remain the same.

How should we evaluate candidates who have different approaches to refactoring?

Establish clear evaluation criteria focused on outcomes rather than specific approaches. Good refactoring should improve code readability, maintainability, and performance regardless of the specific techniques used. Look for candidates who can clearly articulate the reasoning behind their decisions and demonstrate an understanding of tradeoffs, rather than those who simply follow a particular methodology.

Should we expect candidates to complete all the refactoring in the allotted time?

No, these exercises are designed to evaluate approach and decision-making rather than speed. A candidate who thoughtfully refactors a smaller portion of code, clearly explaining their process and the reasoning behind AI tool usage, may demonstrate more valuable skills than someone who rushes through the entire sample without careful consideration.

How can we ensure these exercises don't disadvantage candidates with less AI tool experience?

Structure the exercises to evaluate fundamental refactoring knowledge alongside AI tool usage. Provide clear documentation for any tools you expect candidates to use, and consider offering a brief tutorial or practice session before the formal assessment. Remember that candidates with strong software engineering fundamentals can quickly adapt to AI tools, even with limited prior exposure.

What if our team is still developing our own best practices for AI-assisted coding?

These exercises can actually help your team refine your own approach to AI-assisted development. Pay attention to innovative approaches candidates bring to the table—you might discover techniques that could benefit your existing processes. Consider framing one of the exercises as a collaborative exploration of how AI tools could best integrate into your specific workflow.

AI-assisted code refactoring represents a powerful evolution in software development practices, enabling teams to modernize codebases more efficiently while maintaining high quality standards. By incorporating these work samples into your hiring process, you'll identify candidates who can effectively leverage AI tools while maintaining the critical human judgment necessary for successful refactoring projects.

The most valuable developers in this space understand that AI tools are powerful assistants rather than replacements for human expertise. They know when to trust AI suggestions, when to modify them, and when human creativity and contextual understanding are essential. These exercises will help you find candidates who strike that balance effectively, bringing both technical excellence and thoughtful AI integration to your development team.

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