The hardest part wasn't the coding, it was figuring out where to even start reading.
Start by clarifying the requirements and codebase context, then systematically compare the two to identify gaps and underperforming features. Prioritize the most impactful gaps, design a CLI tool that addresses them, and implement iteratively with tests to ensure correctness and maintainability.
Pro tip: Demonstrate a bias for action by proposing a minimal viable CLI first, then iterating based on feedback—this shows you can deliver value quickly while managing ambiguity.
Ask questions to understand the requirements document's scope and the existing codebase's architecture, dependencies, and current feature set.
Map each requirement to existing code to identify missing features and underperforming areas, documenting findings and potential root causes.
Prioritize gaps based on impact and effort, then design the CLI tool's interface, commands, and integration points with the existing codebase.
Build the CLI in small increments, starting with core functionality, and write tests to validate each feature against the requirements.
Test the CLI end-to-end, gather feedback, and refine the implementation to ensure it meets requirements and performs well.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about entry points and dependency flow, which landed okay.
Start by explaining that you begin with the entry points and high-level architecture to build a mental map, then drill down into critical paths and data flow. Emphasize a systematic, tool-assisted approach that balances breadth and depth, and highlight how you validate understanding by making small changes or writing tests.
Pro tip: Mention that you look for the 'seams' of the system—interfaces, APIs, and configuration files—because they reveal how components interact and where you can safely make changes. Also, note that you timebox exploration to avoid getting lost in details.
Read the README, architecture docs, and any onboarding guides to understand the project's purpose, setup, and high-level structure. This gives you context before diving into code.
Locate the main entry points (e.g., main function, server startup, CLI commands) and examine the build configuration (e.g., Makefile, package.json, pom.xml) to see dependencies and how the project is assembled.
Pick a key feature or user journey and trace its execution path through the code, using debuggers or logging to follow the flow. This reveals the core components and their interactions.
Review unit and integration tests to understand expected behavior, edge cases, and how components are meant to be used. Tests often serve as executable documentation.
Implement a minor fix or add a test to confirm your understanding, then run the test suite. This active learning solidifies your mental model and reveals gaps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked a couple of spots that had obvious duplication and one function that was doing way too many things.
Start by acknowledging that refactoring decisions should be driven by data and business priorities, not personal preference. Then walk through a structured evaluation of code smells, risk, and impact, and propose a prioritized refactoring plan with clear reasoning. Emphasize incremental, safe changes with tests and metrics to validate improvements.
Pro tip: Tie every refactoring suggestion to a measurable outcome (e.g., reduced bug rate, faster feature delivery, lower cloud cost) and mention how you'd validate it with metrics and tests. This shows you think like an engineer who delivers business value, not just clean code.
Look for code smells, high-churn files, frequent bug hotspots, and areas that slow down development. Use tools like static analysis, code coverage, and version control history to find refactoring targets.
Evaluate each candidate's business impact, technical debt, and risk of change. Consider factors like criticality, test coverage, and dependencies to prioritize.
Select the top 1-3 areas to refactor first, explaining why they matter most. Outline a step-by-step approach, including how you'll ensure safety (e.g., tests, feature flags, incremental changes).
Specify how you'll measure the success of each refactoring, such as reduced cycle time, fewer defects, improved performance, or easier onboarding.
Explain how you'll communicate the plan to stakeholders, get buy-in, and adjust based on feedback. Emphasize continuous improvement and learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through unit tests for the core logic and mentioned integration tests for the CLI interface itself.
Start by outlining a layered testing strategy that covers unit, integration, and end-to-end tests, emphasizing automation and CI/CD. Then explain how you would specifically test new features while ensuring existing functionality remains intact through regression tests. Conclude by discussing trade-offs and how you prioritize tests based on risk and impact.
Pro tip: Mention that you would use contract tests or golden files to verify CLI output format, as CLI tools often have strict output expectations that are easy to break. Also, highlight the importance of testing error handling and edge cases, not just happy paths.
Identify all commands, options, inputs, outputs, and exit codes. Map out the expected behavior and edge cases for both new and existing features.
Define unit tests for individual functions, integration tests for command execution and I/O, and end-to-end tests for real-world scenarios. Ensure a balanced mix to catch bugs at different levels.
Set up automated test runs on every commit or pull request. Use tools like pytest, Jest, or shell-based test frameworks, and include coverage reporting.
Write focused tests for new functionality, then run the full suite to ensure no regressions. Use techniques like snapshot testing for output and mocking for external dependencies.
Review test results, measure coverage, and refine tests based on failures and feedback. Consider performance and security testing if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.