I went with the obvious split: intent parsing, dialog management, response generation, persistence.
Start by clarifying the chatbot's responsibilities and identifying natural seams based on domain-driven design and separation of concerns. Then propose a modular architecture with clear interfaces, explaining how you would incrementally refactor the monolith while maintaining functionality.
Pro tip: Emphasize that boundaries should be drawn around business capabilities, not technical layers, and that you'd validate boundaries by measuring coupling and cohesion before and after refactoring.
Map the current codebase: identify core responsibilities, data flows, dependencies, and pain points. Talk to stakeholders to understand business capabilities.
Apply domain-driven design to find bounded contexts (e.g., conversation management, NLU, response generation, user profile, analytics). Group related functionality that changes together.
For each module, specify clear APIs, data schemas, and communication patterns (sync/async). Ensure loose coupling and high cohesion.
Prioritize modules by business value and technical risk. Use strangler fig pattern to gradually extract modules, starting with least coupled. Maintain backward compatibility.
Measure coupling, cohesion, and performance. Gather feedback and adjust boundaries as needed. Automate testing and deployment for each module.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through constructor injection and why I'd avoid a service locator.
Start by clarifying the current architecture and pain points, then propose an incremental, risk-managed introduction of dependency injection (DI) using a lightweight container or manual injection. Emphasize how DI enables isolated testing, and discuss trade-offs like complexity and performance.
Pro tip: Advocate for starting with manual dependency injection in a few critical components to demonstrate value before adopting a full framework, as this minimizes disruption and builds team buy-in.
Identify tightly coupled components, hard-coded dependencies, and testing pain points to prioritize where DI will have the most impact.
Decide between manual DI, a lightweight container, or a full framework based on team size, project complexity, and long-term maintainability.
Start with a few high-value components, refactor them to accept dependencies via constructor or method injection, and write tests to validate isolation.
Document DI conventions, provide examples, and integrate DI into the development workflow to ensure consistency across the codebase.
Track improvements in test coverage, test speed, and developer productivity, and adjust the DI strategy based on feedback and metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer from me: anything environment-specific or likely to change without a code deploy goes in config.
Start by emphasizing a systematic, incremental approach: audit the codebase to identify hardcoded values, categorize them by volatility and environment-dependence, then extract only those that truly benefit from externalization. Balance the trade-off between flexibility and complexity, and highlight the importance of testing and documentation.
Pro tip: Mention that not all hardcoded values should be moved to config—constants that are truly invariant and part of the domain logic (e.g., mathematical constants, protocol limits) are better left in code for clarity and performance. This shows you understand the nuance and avoid over-engineering.
Search for hardcoded values and classify them by type: environment-specific (URLs, credentials), operational (timeouts, retries), business rules (tax rates), and true constants (pi, HTTP status codes).
Decide what belongs in config based on whether the value changes across environments, deployments, or over time without code changes, and whether it needs to be tuned by non-developers.
Prioritize extraction based on impact and risk: start with high-value, low-risk items (e.g., API keys) and defer low-value or high-risk changes. Create a phased plan to avoid disrupting the codebase.
Introduce a configuration management system (e.g., environment variables, config files, feature flags) with validation, defaults, and documentation. Ensure backward compatibility and use feature toggles for safe rollout.
Add tests to verify config loading and behavior, monitor for issues, and gather feedback. Continuously refine the criteria as the system evolves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Started with characterization tests to lock down existing behavior before touching anything, then refactor toward seams you can actually inject.
Start by emphasizing an incremental, risk-driven approach: identify high-value, low-coupling areas to test first, then use seams and dependency injection to isolate units. Show how you balance short-term wins with long-term testability improvements, and tie decisions to trade-offs like coverage vs. velocity.
Pro tip: Frame testing as a tool for enabling safe refactoring, not just bug catching—this shows you understand that testability and design are intertwined. Also, mention that you'd track metrics like defect escape rate to justify the investment to stakeholders.
Map the codebase to find critical paths, high-risk areas, and natural seams (e.g., pure functions, boundaries). Prioritize based on business impact and change frequency.
Use techniques like dependency injection, wrapping legacy code, or extracting interfaces to create testable units without rewriting everything. Start with characterization tests to capture current behavior.
Begin with unit tests for isolated logic, then add integration tests for critical flows. Use test doubles (mocks, stubs) to break dependencies and keep tests fast and reliable.
As tests provide safety, refactor code to improve design (e.g., separate concerns, reduce coupling). This makes future testing easier and improves overall code quality.
Integrate tests into CI/CD, track coverage and defect rates, and iterate. Use metrics to demonstrate value and guide further testing efforts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.