Started here and it was actually a decent warmup.
Start by running the tests to identify failures, then systematically debug each issue by tracing the code and understanding the intended behavior from the tests and documentation. Fix bugs one at a time, ensuring that changes are minimal and do not alter the intended behavior, and re-run tests after each fix to confirm progress.
Pro tip: Before making any changes, read the test cases thoroughly to understand the expected behavior; they are your specification. Also, use a debugger or print statements to trace the code's execution rather than guessing.
Read the problem statement, the code, and the unit tests to grasp the intended behavior and identify what the tests expect. Run the tests to see which ones fail and get initial error messages.
For each failing test, trace the code execution to locate the root cause. Use debugging tools or add temporary logging to inspect variables and control flow.
Make minimal changes to fix one bug at a time, ensuring the fix aligns with the intended behavior. Avoid altering unrelated code to prevent introducing new issues.
After each fix, re-run the tests to confirm the specific failure is resolved and no regressions occur. Continue until all tests pass.
Once all tests pass, review the changes for clarity and maintainability. If time permits, consider if any refactoring can improve the code without changing behavior.
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 cell is a node and edges connect adjacent open cells. Use BFS with a queue to explore level by level, tracking visited cells and distances, and return the distance when the exit is reached or -1/None if the queue empties.
Pro tip: Clarify the maze representation and movement rules upfront (e.g., 4-directional vs 8-directional, start/exit symbols) to avoid incorrect assumptions. Mention that BFS guarantees the shortest path in unweighted graphs, and consider edge cases like start equals exit or no path.
Ask about the maze format (2D array, characters), movement directions, and what constitutes a valid path. Confirm return type for no path (-1 or None).
Initialize a queue with the start cell and a visited set or distance matrix. Define directions (e.g., up, down, left, right).
While the queue is not empty, dequeue a cell, check if it's the exit, and if not, enqueue all valid unvisited neighbors with distance+1.
If exit is found, return its distance. If queue empties without finding exit, return -1 or None as specified.
State time and space complexity: O(R*C) for both, where R and C are maze dimensions, since each cell is visited at most once.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the directional constraints and how they affect valid moves from each cell. Then, modify the neighbor function to only return moves that satisfy those constraints, and run BFS to find the shortest feasible path. Discuss trade-offs such as handling unreachable targets and potential optimizations.
Pro tip: Explicitly state that BFS remains optimal because all moves have equal cost, and mention that if constraints are dynamic or weighted, you might need Dijkstra or A*. This shows you understand algorithm selection beyond the basics.
Ask clarifying questions to understand the exact directional rules (e.g., can you only move right and down? Are there forbidden turns?) and confirm that the goal is still shortest path in terms of number of moves.
Update the neighbor function to generate only moves that comply with the directional constraints. Ensure it checks the current direction (if stateful) or simply filters based on allowed directions from the current cell.
Use BFS for unweighted grids to guarantee shortest path. Explain that BFS explores level by level, so the first time you reach the target, it's via the shortest feasible path.
Consider cases where no path exists due to constraints, and return an appropriate value (e.g., -1 or empty list). Also handle start equals target and out-of-bounds moves.
State time and space complexity (O(V+E) for BFS). Mention possible optimizations like bidirectional BFS or A* if heuristics are available, and discuss trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The bitmask thing for tracking keys is something I'd seen before but blanked on under pressure and started reaching for a frozenset.
Model the state as (row, col, keys_bitmask) where keys_bitmask is a 6-bit integer representing collected keys. Run BFS over this expanded state space, updating the bitmask when stepping on a key and only moving through a door if the corresponding bit is set. Return the distance when reaching the exit.
Pro tip: Emphasize that the state space is bounded by rows × cols × 2^6, so BFS remains efficient; also mention that you can optimize by only tracking keys that are actually present in the maze.
Represent each state as (row, col, keys_bitmask), where keys_bitmask uses bits 0-5 for keys a-f. This captures all necessary information to determine valid moves.
Start BFS from the start position with an empty key bitmask (0). Use a queue to store states and a visited set or 3D array to track visited (row, col, bitmask) combinations.
For each state, explore four directions. If the next cell is a wall, skip. If it's a key, update the bitmask by setting the corresponding bit. If it's a door, only proceed if the matching key bit is set. Otherwise, move normally.
Maintain a distance counter (e.g., level-order traversal). When the exit cell is reached, return the current distance as the shortest path length.
If BFS exhausts all states without reaching the exit, return -1 or indicate no path exists.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.