Software Engineering Practices Topics
Covers industry-standard practices for building maintainable, high-quality software, including code quality, maintainability, documentation, and effective technical communication within engineering teams.
Engineering Quality and Best Practices
Focuses on the practices, standards, and oversight that keep code maintainable, reliable, and testable over time. Candidates should be able to discuss testing strategies, documentation practices, refactoring approaches, static analysis and linters, continuous integration and continuous delivery pipelines, and metrics for code health and maintainability. This topic also covers how to set and enforce code review standards, provide technical oversight, manage technical debt pragmatically, and identify and lead technical or process improvements that raise team productivity and product quality.
Code Quality and Communication
Skills and practices for producing readable, maintainable, and idiomatic code while clearly communicating intent. Candidates should demonstrate clear naming, logical structure, proper error handling, and writing code that other engineers can easily review and extend. This topic also covers narrating your thought process while coding, explaining trade offs between readability and optimization, identifying next optimization steps, and knowing when to avoid premature optimization. Interviewers will assess both the code you produce and your ability to explain design choices and sequencing of improvements.
Production Grade Code
Focuses on writing code that is safe, maintainable, and operable in production environments. Topics include defensive programming, robust error handling and retry strategies, idempotency, resource management, structured logging and metrics for observability, health checks and graceful degradation, testability with unit and integration tests, performance and memory considerations, dependency management and versioning, feature flag strategies and safe deployment patterns, and processes for validating and rolling back changes in production. Emphasis is on choices that reduce operational burden and support long term maintainability.
Reading and Understanding Code
Demonstrate the ability to quickly understand unfamiliar code by locating entry points, following control flow, identifying key data structures and invariants, and summarizing the overall purpose and boundaries of a module. Good strategies include reading tests or example usage first, tracing a simple input through the code, sketching the data flow, and proposing small safe edits or tests rather than broad rewrites.
Incremental Feature Addition
Focus on designing and implementing new functionality incrementally inside an existing code base. Discuss how to identify the right extension points, follow established patterns, add unit and integration tests, maintain backward compatibility, and use feature flags or phased rollouts when appropriate. Emphasize avoiding large rewrites, planning migrations safely, coordinating with stakeholders, and how to plan rollbacks or hot fixes.
Reading and Understanding Unfamiliar Code
Test the ability to quickly comprehend and reason about unknown code. Candidates should demonstrate techniques for tracing execution flow identifying key data structures and control paths using tests and logging to infer behavior spotting anomalies and potential bugs and proposing safe minimal changes or refactors. Strong answers describe how to build a mental model of the code base use available documentation and tests and validate assumptions before making changes.
Technical Debt and Delivery Tradeoffs
Assess judgment about when to prioritize shipping versus investing in long term maintainability. Topics include identifying and classifying technical debt quantifying maintenance and operational cost prioritization frameworks for choosing which debt to pay down mitigation strategies such as tests and automated checks refactoring patterns and how to communicate and schedule debt reduction. Interviewers look for examples of trade off analysis risk mitigation and metrics used to justify choices.