Not too bad once I figured out what they were actually asking.
First, identify that the problem is a dependency graph and determine whether it requires topological sorting, cycle detection, or shortest path. Then, choose an efficient algorithm like Kahn's or DFS with memoization, and implement it carefully while handling edge cases such as cycles and disconnected components.
Pro tip: In timed assessments, start by writing a brute-force solution to pass sample tests, then optimize if time permits. Always test with edge cases like empty input, single node, and cycles to avoid hidden failures.
Clarify what the graph represents (e.g., tasks with dependencies) and what output is required (e.g., valid order, shortest path). Identify constraints like node count and edge count to gauge required efficiency.
Choose an appropriate representation: adjacency list for sparse graphs or adjacency matrix for dense graphs. Decide if the graph is directed/undirected and whether weights are involved.
Match the problem to a known graph algorithm: topological sort for dependency ordering, DFS/BFS for cycle detection, Dijkstra for weighted shortest path, etc. Consider time and space complexity.
Write clean code with meaningful variable names. Test with provided examples and edge cases (empty graph, single node, cycle, disconnected components) to ensure correctness.
If time permits, review for potential optimizations (e.g., using iterative DFS to avoid recursion limit, early termination). Ensure the solution meets the time limit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by reading the technical spec to understand the expected behavior, then run the failing tests to see the errors. Use a systematic debugging approach: reproduce, isolate, hypothesize, fix, and verify. Prioritize fixes that unblock multiple tests and manage time by tackling high-impact issues first.
Pro tip: Before diving into code, check if the repository has a README or setup script to ensure the environment is correctly configured; many failures stem from missing dependencies or misconfigurations. Also, use git bisect or recent commits to identify when tests started failing, which can quickly point to the root cause.
Read the technical spec thoroughly to grasp the expected functionality. Run the failing tests to capture error messages and stack traces, noting which tests fail and why.
Reproduce failures locally in a controlled environment. Isolate the problem by narrowing down to specific modules or functions using debugging tools or logging.
Based on the spec and errors, form hypotheses about the root cause. Test each hypothesis by making minimal changes and re-running tests to confirm or refute.
Apply targeted fixes, ensuring they align with the spec and don't break other tests. Run the full test suite to verify all tests pass.
If time permits, refactor code for clarity and add comments or documentation to explain the fix, demonstrating good engineering practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.