Started with brute force, scanning all four directions for each robot until hitting an X or the boundary.
Precompute distance profiles for all cells using four directional passes, then answer each query by checking robots whose profiles match. Optimize with hashing or grouping to handle multiple queries efficiently.
Pro tip: Mention that you can group robots by their distance profile to answer multiple queries in O(1) each, showing awareness of scalability for large grids and many queries.
Confirm grid dimensions, query frequency, and whether multiple queries need to be answered. Discuss input/output formats and edge cases like no robots or no matches.
For each cell, compute the distance to the nearest blocker in all four directions using dynamic programming: left-to-right, right-to-left, top-to-bottom, and bottom-to-top passes.
Store robots in a hash map keyed by their distance profile (tuple of four distances) to enable fast lookup for any query.
For each query, retrieve the list of robots with the matching profile from the hash map and return their positions. If no match, return an empty list.
Discuss time and space complexity: O(R*C) preprocessing, O(1) per query, and O(R*C) space. Mention alternatives like on-the-fly computation if memory is constrained.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the function's contract: inputs, outputs, and edge cases. Then systematically cover normal cases, boundary conditions, invalid inputs, and performance considerations, organizing tests by category.
Pro tip: Mention that you'd write tests to be deterministic and independent, and consider using property-based testing for matching logic to catch unexpected edge cases.
Ask about input types, expected output, and any constraints (e.g., time complexity, matching criteria). This ensures tests align with requirements.
Break down tests into normal cases, edge cases, invalid inputs, and performance. This provides comprehensive coverage.
For each category, list concrete examples: e.g., empty input, single element, multiple matches, no matches, duplicate elements, large input.
Include tests for performance (e.g., large input), determinism (same input yields same output), and idempotence if applicable.
Highlight which tests are most critical and why, showing an understanding of risk and impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.