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I went with BFS, kept a visited set to handle cycles, and iterated through neighbors.
Clarify the problem and edge cases, then propose a graph traversal (BFS or DFS) to determine connectivity. Implement the solution with careful handling of missing stations and discuss complexity.
Pro tip: Mention that BFS is often preferred for path existence because it finds the shortest path and can terminate early, but DFS is simpler and uses less memory. Also, highlight the importance of treating missing stations as isolated nodes.
Ask if the graph is directed or undirected, and confirm that missing stations should be treated as having no connections. Discuss edge cases like same start and end, or either station missing.
Select BFS or DFS based on trade-offs. BFS is good for shortest path and early exit; DFS is simpler and uses less memory.
Write a function that uses a queue (BFS) or stack (DFS) and a visited set to avoid cycles. Check if start or end is missing and return false if so.
State time and space complexity (O(V+E) time, O(V) space). Walk through test cases including disconnected graphs and missing stations.
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 and graph representation, then systematically design test cases for each specified scenario, including edge cases and expected outcomes. Prioritize tests that validate correctness and robustness, and discuss how to handle ambiguous cases like missing stations.
Pro tip: Demonstrate maturity by discussing not just test cases but also how you'd structure them (e.g., using a table-driven approach) and what each test reveals about the algorithm's assumptions. Mention that missing stations should be treated as an error condition unless specified otherwise, and test both start and end missing.
Ask about the graph representation (adjacency list/matrix), whether the graph is directed/weighted, and the expected behavior for missing stations (error vs. empty path).
For cycles, ensure the algorithm doesn't loop infinitely and returns a valid path; for disconnected components, expect no path; for start equals end, expect a trivial path; for missing stations, expect an error or empty result.
Test empty graph, single node, start or end missing, and large graphs to check performance. Also test graphs with multiple paths to ensure shortest path is returned if applicable.
For each test, specify the exact expected result (e.g., path list, distance, or exception) and how to assert it, ensuring tests are deterministic.
Explain how you'd structure tests (e.g., parameterized tests) and any mocking needed, and mention tools like JUnit or pytest to show practical experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.