Roblox·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Roblox software engineer interview with a call stack simulation problem. Pretty algorithmic, required walking through the logic carefully and explaining complexity on the spot.

Questions Asked (1)

Given an array of log entries for a single-threaded program where each entry is either '->Name' (function entry) or '<-Name' (function exit), track the call stack after every entry event and return the stack snapshot string that appears most frequently, along with its count. For example, logs like ['->A','->B','->C','<-C','->C','<-C','<-B','<-A'] should return 'A->B->C' with a count of 2.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just simulate the stack and use a hashmap to count snapshots, which is basically the right answer.

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AI HintsAI Generated

Suggested Approach

Simulate the call stack using a list, updating it on each log entry and recording the stack state after each event. Use a hash map to count the frequency of each stack snapshot, then find the snapshot with the highest count. Handle edge cases like empty logs or multiple snapshots with the same maximum count.

Pro tip: Clarify tie-breaking rules upfront: if multiple stack snapshots have the same maximum frequency, ask whether to return the first encountered or any. Also, discuss time and space complexity: O(n) time and O(n) space in the worst case.

1. Understand the problem and clarify edge cases

Confirm input format, expected output, and how to handle ties or empty logs. Ask if the stack snapshot should include the full stack or just the top.

2. Design the simulation and counting approach

Use a stack (list) to track function calls. For each log entry, push on '->' and pop on '<-'. After each update, convert the stack to a string and increment its count in a hash map.

3. Implement the solution

Iterate through logs, update stack, and record snapshots. After processing, iterate through the hash map to find the snapshot with the highest count.

4. Analyze complexity and discuss trade-offs

Explain that time complexity is O(n * m) where m is average stack depth for string conversion, but can be optimized. Space complexity is O(n) for the hash map and stack.

5. Test with examples and edge cases

Walk through the given example and test cases like empty logs, single function, nested calls, and ties. Verify correctness.

Key Points to Mention

Stack simulation using a list with push/pop operations.
Hash map to count frequency of each stack snapshot string.
Time complexity: O(n * m) due to string conversion, where m is stack depth; can be optimized with hashing or persistent data structures.
Space complexity: O(n) for storing snapshots and counts.
Edge cases: empty logs, ties in frequency, and malformed logs (though problem likely guarantees valid logs).
Tie-breaking: clarify whether to return the first most frequent or any.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.