Three debugging sub-questions back to back.
Start by understanding the maze representation, movement rules, and expected output. For each broken version, reproduce the failure with a small test case, then trace the algorithm step-by-step to isolate the root cause. Fix the issue and verify with edge cases like empty mazes, unreachable goals, and loops.
Pro tip: Meta interviewers value structured debugging over quick fixes. Verbalize your hypotheses and systematically eliminate them, showing you can debug under pressure and communicate clearly.
Ask about maze dimensions, movement directions, start/end points, and expected output (path existence or actual path). Confirm edge cases like empty maze or no solution.
For each broken version, run a minimal test case that triggers the failure. Note the observed vs. expected behavior to narrow down the issue.
Use print statements or a debugger to trace variable states and control flow. Identify the exact line(s) causing incorrect behavior, considering common pitfalls like off-by-one errors, missing visited checks, or wrong boundary conditions.
Apply the minimal fix, then re-run the failing test and additional edge cases to ensure correctness and no regressions.
Briefly explain each bug and fix, and mention how you would prevent similar issues in the future (e.g., unit tests, code reviews).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the maze as a graph where each state includes the current position and the set of keys collected. Use BFS to find the shortest path, updating the state when keys are picked up and checking for doors that require specific keys. Discuss trade-offs between state space size and efficiency, and consider optimizations like bitmask for keys.
Pro tip: Mention that the state space can grow exponentially with the number of keys, so using a bitmask to represent keys and a visited set for each (position, keymask) pair is crucial for efficiency. Also, clarify that BFS remains optimal because each move has uniform cost.
Ask about the maze representation, number of keys, whether keys are reusable, and if multiple keys can be held. Confirm that the goal is to find the shortest path to the exit.
Represent each state as (row, col, keys_collected), where keys_collected is a bitmask of acquired keys. This captures all necessary information to determine valid moves.
Use BFS because each move has uniform cost. Maintain a queue of states and a visited set to avoid revisiting the same state. When encountering a key, update the bitmask; when encountering a door, check if the corresponding key bit is set.
Discuss time and space complexity: O(R*C*2^K) where K is number of keys. Mention that this is acceptable for small K but may need optimization for large K. Compare with alternative approaches like Dijkstra if moves have varying costs.
Walk through examples: no keys, unreachable exit, multiple keys, doors without keys. Ensure the algorithm handles cycles and correctly updates the visited set.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.