The part I underestimated was the justification.
Start by restating the code's purpose in plain English, then systematically analyze its time and space complexity with clear justifications. Identify edge cases and inefficiencies, and propose a more efficient version if possible, explaining the trade-offs.
Pro tip: Always connect your analysis to real-world scenarios at Asana, such as scalability and performance, to show you think beyond just the code.
Read the code carefully and explain its functionality in simple terms, as if to a non-technical stakeholder.
Determine the time and space complexity using Big O notation, justifying each by referencing specific operations in the code.
List potential edge cases (e.g., empty inputs, large data) and point out any inefficiencies in the current implementation.
Suggest a more efficient algorithm or data structure, explaining how it improves time or space complexity and any trade-offs.
Recap the key points and emphasize the importance of the optimization in the context of the role or company.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.