Uber·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Uber software engineering interview that went deep into testing methodology for word-search algorithms. The question was less about writing code and more about how you think about coverage, failure modes, and test architecture at scale.

Questions Asked (1)

Design a full test plan and TDD workflow for two word-search functions, covering unit tests, property-based tests, and stress tests. Include concrete cases like empty inputs, all eight directional matches, diagonal-only paths, boundary crossings, repeated letters, multiple valid start positions, and large inputs for performance and stack depth.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This one sprawled in a way I didn't expect.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the two functions' signatures and expected behavior, then structure your answer around a layered test strategy: unit tests for correctness, property-based tests for invariants, and stress tests for performance and stack safety. Walk through concrete edge cases and explain how TDD drives the design, including red-green-refactor cycles and trade-offs between exhaustive and randomized testing.

Pro tip: Emphasize that property-based tests should encode invariants like 'if a word is found, reversing the grid and word preserves findability' and 'no false positives on random grids'—this shows you think beyond example-based tests and understand how to catch subtle bugs at scale.

1. Clarify requirements and function contracts

Ask about input types (grid dimensions, word length), return values (boolean, list of paths, count), and constraints (case sensitivity, overlapping matches). Define the two functions' exact signatures and expected behavior before writing any tests.

2. Design unit tests for core correctness

Enumerate concrete cases: empty grid, empty word, single-cell grid, all eight directions (N, NE, E, SE, S, SW, W, NW), diagonal-only paths, boundary crossings, repeated letters, and multiple valid start positions. Write these as table-driven tests and implement them first in a TDD cycle.

3. Add property-based tests for invariants

Use a property-based testing library (e.g., Hypothesis, QuickCheck) to generate random grids and words. Assert invariants such as: found words remain found after grid rotation/reflection, no false positives on random noise, and the count of matches equals the number of valid paths.

4. Implement stress tests for performance and stack depth

Generate large grids (e.g., 1000x1000) and long words to measure time and memory. Test recursive implementations with deep paths to catch stack overflow, and compare iterative vs. recursive approaches. Set performance budgets and run under profiling.

5. Integrate TDD workflow and iterate

Follow red-green-refactor: write a failing test, implement minimal code to pass, then refactor. Use the test suite to guide design decisions (e.g., choosing DFS vs. BFS, early termination). Continuously run all tests and add regression tests for any bugs found.

Key Points to Mention

Eight-directional search: explicitly test each direction and combinations, including diagonal-only paths and boundary crossings.
Edge cases: empty inputs, single-character words, repeated letters causing multiple matches, and overlapping paths.
Property-based testing: invariants like rotation/reflection symmetry, no false positives, and match count consistency.
Stress testing: large grids for time/memory, deep recursion for stack depth, and iterative alternatives.
TDD cycle: red-green-refactor, test-first design, and using tests to drive API and algorithm choices.
Trade-offs: exhaustive vs. randomized testing, recursion vs. iteration, and early termination vs. full search.

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