Debugging someone else's code under pressure is its own skill and I don't practice it enough.
Start by running the provided unit tests to identify failing cases, then trace the code to locate logical errors in traversal, boundary checks, and visited-state handling. Fix the minimal set of issues to make tests pass, explaining your debugging process and verifying edge cases.
Pro tip: Before changing any code, articulate the expected behavior of each test and use a debugger or print statements to confirm your hypothesis about the bug. This shows structured debugging and avoids random edits.
Read the problem statement and the provided unit tests to clarify the maze representation, movement rules, and expected outputs. Identify which tests are failing and what they assert.
Run the tests to see the exact failures, then add logging or use a debugger to trace the solver's execution on a minimal failing case. Narrow down the bug to a specific function or condition.
Compare the code's logic against the correct algorithm (e.g., BFS/DFS) and check for common mistakes: off-by-one boundaries, missing visited checks, incorrect neighbor generation, or wrong start/target handling.
Make the minimal code change to correct the bug, then re-run all tests to ensure they pass. If needed, add temporary assertions or edge-case tests to confirm robustness.
Summarize the bug, your fix, and how you verified it. Mention any trade-offs (e.g., time/space complexity) and potential improvements for production code.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the maze as an unweighted graph where each cell is a node and edges connect adjacent open cells. Use BFS from the start cell S, tracking distances, and return the distance when T is reached or -1 if the queue empties without finding T.
Pro tip: Clarify edge cases upfront (e.g., S == T, no path, invalid input) and mention that BFS guarantees shortest path in unweighted graphs, showing you understand the algorithm's properties.
Ask about maze representation (grid of chars, 0/1, etc.), movement allowed (4-directional or 8-directional), and whether S and T are guaranteed to exist. Confirm that each move costs 1.
Use a queue for BFS, a 2D array or hash set to track visited cells, and a distance map or store distance in the queue. Initialize with S and distance 0.
While queue is not empty, dequeue a cell, check if it's T (return distance), and enqueue all valid unvisited neighbors with distance+1. Mark visited when enqueuing to avoid duplicates.
If T is never reached, return -1. Also handle edge case where S == T by returning 0 immediately.
State time and space complexity: O(R*C) for both, where R and C are maze dimensions. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the movement restrictions and how they interact with the existing maze solver. Then, model the problem as a state-space search where the state includes the current cell and the previous move direction, and adapt BFS/DFS accordingly. Discuss trade-offs between different approaches and ensure the solution handles edge cases.
Pro tip: Explicitly define the state representation early and discuss how it affects complexity; this shows you understand that adding constraints often requires augmenting the state, a key insight for scalable solutions.
Ask questions to fully understand the movement restrictions: Are they based on the previous move direction, current cell, or both? Are they deterministic? Can they be precomputed?
Determine what information is needed to represent the state. Typically, this includes the current cell and the direction of the previous move (or a sentinel for the start).
Modify BFS or DFS to incorporate the restrictions. When exploring neighbors, check if the move is allowed based on the current state and the rules.
Discuss how the state space size changes (e.g., from O(V) to O(V*D) where D is number of directions). Compare BFS vs DFS and consider optimizations like bidirectional search or A* if applicable.
Walk through edge cases: start cell, goal cell, dead ends, cycles. Ensure the solution correctly handles restrictions and terminates.
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 to the target, since each move has uniform cost. When encountering a door, only proceed if the corresponding key is in the collected set.
Pro tip: Mention that the state space can be optimized by only tracking keys that are actually present in the maze, and use bitmasking to represent key sets efficiently. Also, discuss how to handle multiple keys of the same type and doors that may be opened later.
Represent each state as (row, col, keys_collected), where keys_collected is a bitmask or set of keys obtained so far. This captures all necessary information to determine valid moves.
Start BFS from the initial position with an empty key set. Use a queue to process states level by level, ensuring the first time we reach the target is the minimum moves.
For each state, explore all four directions. If the next cell is a wall, skip. If it's a key, add it to the key set. If it's a door, only proceed if the matching key is held.
Use a visited set to avoid revisiting the same (row, col, keys) state. This prevents cycles and ensures efficiency.
If BFS reaches the target, return the number of moves. If the queue is exhausted without reaching the target, return -1 to indicate it's impossible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.