Effective Work Samples for Evaluating AI Technical Debt Prioritization Skills

Technical debt is a critical challenge in software development, representing the accumulated cost of choosing expedient solutions over optimal ones. As codebases grow in complexity, identifying and prioritizing technical debt becomes increasingly difficult. Artificial intelligence offers powerful new approaches to tackle this challenge, enabling organizations to systematically identify, quantify, and address technical debt at scale.

Professionals skilled in AI-driven technical debt prioritization combine expertise in machine learning, software engineering, and strategic decision-making. They must understand how to leverage AI algorithms to analyze code quality, identify patterns of technical debt, and make data-driven recommendations about which issues to address first. These individuals serve as crucial bridges between technical teams and business stakeholders, translating complex technical concepts into clear business impact.

When hiring for roles requiring AI technical debt prioritization skills, traditional interviews often fall short. Candidates may excel at discussing theoretical approaches but struggle to apply them in real-world scenarios. Well-designed work samples provide a window into how candidates actually approach these complex problems, revealing their technical abilities, analytical thinking, and communication skills.

The following work samples are designed to evaluate a candidate's proficiency in using AI to manage technical debt. Each exercise simulates realistic challenges they would face on the job, from designing AI systems that detect technical debt to communicating findings effectively to stakeholders. By observing candidates work through these exercises, hiring teams can gain valuable insights into their potential performance and fit for roles requiring this specialized skillset.

Activity #1: Technical Debt Detection System Design

This activity evaluates a candidate's ability to design an AI system for detecting and classifying technical debt in a codebase. It tests their understanding of machine learning approaches, software metrics, and system architecture. This foundational skill is essential for anyone working on AI-driven technical debt prioritization, as it forms the basis for all subsequent analysis and decision-making.

Directions for the Company:

• Provide the candidate with a description of a fictional company's codebase (e.g., "A 5-year-old microservices architecture with 50+ services written primarily in Python and JavaScript, with some legacy Java components").
• Share sample code metrics data in CSV or JSON format showing basic statistics about the codebase (lines of code, commit frequency, test coverage, etc.).
• Allow the candidate 45-60 minutes to complete their system design.
• Prepare to evaluate their understanding of both AI concepts and technical debt indicators.

Directions for the Candidate:

• Design an AI system that could automatically detect and classify different types of technical debt in the provided codebase.
• Create a diagram showing the system architecture, including data sources, processing pipeline, and output mechanisms.
• Specify which machine learning algorithms or approaches you would use and why.
• Identify the key metrics and indicators your system would track to identify different categories of technical debt.
• Explain how your system would differentiate between critical and non-critical technical debt.

Feedback Mechanism:

• Provide feedback on the candidate's approach, focusing on one strength (e.g., "Your inclusion of both static code analysis and git history as data sources was particularly insightful") and one area for improvement (e.g., "Your model doesn't account for dependencies between services").
• Ask the candidate to revise one aspect of their design based on your feedback, giving them 10-15 minutes to make adjustments.
• Observe how receptive they are to feedback and how effectively they incorporate it into their revised design.

Activity #2: Technical Debt Prioritization Algorithm Implementation

This activity tests the candidate's ability to implement a practical algorithm for prioritizing technical debt using AI techniques. It evaluates their coding skills, understanding of prioritization frameworks, and ability to translate theoretical knowledge into working solutions. This hands-on implementation skill is crucial for moving beyond theoretical discussions to creating actual tools that development teams can use.

Directions for the Company:

• Prepare a dataset of technical debt issues with various attributes (e.g., complexity, affected components, estimated fix time, potential business impact, etc.).
• Provide access to a development environment with necessary libraries (Python with scikit-learn, pandas, etc.).
• Allow 60-90 minutes for the implementation.
• Have a technical team member available to answer clarifying questions about the dataset.

Directions for the Candidate:

• Implement an algorithm that prioritizes the provided technical debt issues based on multiple factors.
• Your algorithm should use at least one machine learning technique (e.g., clustering, classification, or ranking).
• Include both technical factors (code complexity, test coverage) and business factors (impact on user experience, revenue risk) in your prioritization model.
• Provide visualizations that would help engineering leaders understand your prioritization recommendations.
• Write a brief explanation of your approach, including why you chose specific features and algorithms.
• Your solution should output a ranked list of technical debt issues with explanations for their priority level.

Feedback Mechanism:

• Provide feedback on the implementation, highlighting one effective aspect (e.g., "Your weighting of business impact factors was well-reasoned") and one area for improvement (e.g., "The algorithm doesn't account for the interdependencies between issues").
• Ask the candidate to refine one specific aspect of their algorithm based on your feedback, giving them 15-20 minutes to make adjustments.
• Evaluate their ability to quickly iterate on their solution and incorporate new considerations.

Activity #3: Technical Debt ROI Analysis

This activity assesses the candidate's ability to quantify the return on investment for addressing technical debt using AI-driven insights. It tests their financial analysis skills, business acumen, and ability to translate technical metrics into business value. This skill is essential for justifying technical debt remediation efforts to executives and stakeholders who control budgets and resources.

Directions for the Company:

• Provide a scenario describing a software product with significant technical debt issues identified by an AI analysis system.
• Include data on development velocity, bug rates, customer satisfaction, and engineering time allocation.
• Supply a template for ROI calculations that includes costs, benefits, and timeframes.
• Allow 45-60 minutes for the analysis.

Directions for the Candidate:

• Using the AI-generated technical debt analysis provided, develop an ROI model for addressing the identified issues.
• Quantify both the costs (engineering time, potential service disruptions) and benefits (improved velocity, reduced bugs, enhanced features) of technical debt remediation.
• Create a prioritized roadmap for addressing technical debt based on your ROI analysis.
• Develop a visualization that clearly communicates the financial impact of technical debt to non-technical stakeholders.
• Explain how you would use AI to continuously monitor and update your ROI projections as work progresses.
• Recommend specific metrics that should be tracked to validate your ROI predictions.

Feedback Mechanism:

• Provide feedback on their analysis, noting one strength (e.g., "Your inclusion of opportunity cost in the ROI calculation was particularly insightful") and one area for improvement (e.g., "The timeline for realizing benefits seems overly optimistic").
• Ask the candidate to adjust one aspect of their ROI model based on your feedback, giving them 15 minutes to make revisions.
• Evaluate how well they incorporate business realities into their technical analysis and how effectively they respond to feedback.

Activity #4: AI-Driven Technical Debt Communication Exercise

This activity evaluates the candidate's ability to communicate complex technical debt findings from AI analysis to different stakeholders. It tests their communication skills, stakeholder management, and ability to translate technical concepts into business language. This skill is crucial for ensuring that technical debt remediation efforts receive appropriate support and resources from across the organization.

Directions for the Company:

• Prepare an AI-generated technical debt analysis report with visualizations, metrics, and recommendations.
• Define three different stakeholder personas: a CTO, a product manager, and a development team lead.
• Provide information about the company's business goals and constraints.
• Allow 60 minutes for preparation and 15 minutes for the presentation/discussion.

Directions for the Candidate:

• Review the AI-generated technical debt analysis provided.
• Prepare a brief (5-minute) presentation for each of the three stakeholders, highlighting the aspects of the analysis most relevant to their role and concerns.
• For the CTO: Focus on strategic implications, long-term architecture health, and resource allocation.
• For the product manager: Emphasize impact on feature delivery, product quality, and customer experience.
• For the development team lead: Address specific technical challenges, implementation approaches, and team workload.
• Create or adapt visualizations from the AI analysis that effectively communicate your key points to each audience.
• Be prepared to answer questions from the perspective of each stakeholder.

Feedback Mechanism:

• After the candidate presents to one stakeholder persona, provide feedback on their communication approach, noting one strength (e.g., "Your translation of technical metrics into business impact was very effective") and one area for improvement (e.g., "The presentation contained too much technical jargon for this audience").
• Ask the candidate to adjust their approach for the next stakeholder based on your feedback.
• Evaluate how well they tailor their message to different audiences and how effectively they respond to questions and concerns.

Frequently Asked Questions

How long should these work sample exercises take?

Each exercise is designed to take between 45-90 minutes. For a comprehensive assessment, you might choose to use 1-2 exercises as part of your interview process, rather than all four. Select the exercises that best align with the specific responsibilities of your open position.

Should candidates complete these exercises during the interview or as take-home assignments?

Activities #1 and #3 work well as in-person exercises during later interview stages, while Activities #2 and #4 can be effective as take-home assignments with a follow-up discussion. For take-home exercises, we recommend setting clear time expectations and ensuring the scope is reasonable (no more than 2-3 hours of work).

How should we evaluate candidates who have experience with technical debt but are new to AI approaches?

Focus on their problem-solving approach and ability to learn. Look for candidates who demonstrate strong analytical thinking and who can articulate how they would leverage AI capabilities, even if they haven't implemented similar systems before. Consider providing some background materials on AI approaches to technical debt before the exercise.

What if we don't have real technical debt data to share with candidates?

You can create simplified synthetic data that represents typical technical debt patterns in your organization. Alternatively, there are open-source datasets available that represent code metrics and technical debt indicators from public repositories. The key is ensuring the data presents realistic challenges that test the candidate's analytical abilities.

How should we weight technical skills versus communication skills in these exercises?

This depends on the specific role. For a technical lead position focused on implementing AI systems for technical debt, you might weight Activities #1 and #2 more heavily. For roles that interface with business stakeholders, Activities #3 and #4 might be more important. Ideally, candidates should demonstrate competence in both areas, with excellence in the areas most critical to your specific needs.

Can these exercises be adapted for candidates with different levels of experience?

Yes, these exercises can be scaled in complexity. For junior candidates, you might provide more structure and guidance, focus on implementing specific algorithms, or evaluate their ability to explain existing technical debt patterns. For senior candidates, expect more sophisticated system designs, novel approaches to prioritization, and strategic thinking about technical debt management.

AI-driven technical debt prioritization represents a significant opportunity for organizations to manage their codebases more effectively and allocate engineering resources more strategically. By using these work samples in your hiring process, you can identify candidates who not only understand the theoretical aspects of technical debt and AI but can apply these concepts to create real business value.

The right talent in this area can transform how your organization approaches technical debt, moving from reactive firefighting to proactive management guided by data-driven insights. For more resources on building effective technical teams, check out Yardstick's AI job descriptions, AI interview question generator, and AI interview guide generator.

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