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.
Code Quality & Technical Communication
Best practices and principles for writing clean, maintainable code and communicating technical decisions clearly. Topics include code quality metrics, code reviews, refactoring, static analysis, testing strategies related to maintainability, documentation standards, API/documentation practices, and effective communication of design and architecture decisions.
Coding in Collaborative Environments
Practical expectations and skills for writing code in shared environments or during pair programming. Topics include writing clear and modular code, using descriptive names, documenting intent with comments and documentation, structuring code for readability, adding simple tests, and performing quick refactors in a live coding setting. Be prepared to explain your code as you write it, respond to feedback, and follow team conventions such as style guides, code review processes, and continuous integration workflows.
Code Review and Verification
Assess the ability to verify correctness, safety, and maintainability of code including code that was written or suggested by tools. Topics include spotting memory leaks, race conditions, incorrect threading models, platform specific lifecycle mistakes, performance regressions, unclear abstractions, security vulnerabilities, and missing tests. Evaluate review practices such as writing focused review comments, proposing minimal safe fixes, using static analysis and linters, running unit and integration tests, and using profiling tools to confirm performance characteristics.
Problem Solving and Ambiguity Handling
Evaluates how candidates approach ill-defined problems, make decisions with incomplete information, and keep making progress under uncertainty. Covers structuring ambiguous problems into testable hypotheses, running quick experiments or lightweight investigations to gather evidence, prioritizing the next best action, weighing trade-off decisions between speed and confidence, using available data and evidence (not just one kind of tooling) to validate assumptions, and communicating risks and unknowns to stakeholders. Strong answers describe a repeatable framework for triage, concrete mitigation strategies, and a real example where the candidate preserved momentum while actively managing risk.