Felt straightforward at first and I almost rushed into code without thinking about the single-character case.
Start by clarifying the problem constraints and edge cases, then propose a simple linear scan solution that builds the compressed string. Discuss trade-offs such as time/space complexity and potential optimizations like using a StringBuilder or handling large counts.
Pro tip: Mention that you would verify the compressed string can be decompressed back to the original, and discuss how to handle edge cases like empty strings or single-character runs to show thoroughness.
Ask about input constraints (e.g., string length, character set) and confirm expected behavior for empty strings, single characters, and runs of length 1.
Describe a linear scan approach: iterate through the string, count consecutive identical characters, and append the character and count to the result.
State that time complexity is O(n) and space complexity is O(n) for the output. Discuss whether in-place modification is possible or if using a StringBuilder is more efficient.
Explain how to handle empty string, single character, and runs of length 1. Mention potential optimizations like early termination if compressed length exceeds original.
Walk through a few test cases (e.g., 'aaabbc' -> 'a3b2c1', 'abc' -> 'a1b1c1') to verify correctness and discuss how to test the function.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Multi-digit counts are where this gets annoying.
Clarify the exact encoding format (e.g., whether counts precede characters, how multi-digit counts are delimited, and if there are any special cases like zero counts). Then implement a single-pass parser that accumulates digits into a count and appends the repeated character to a result builder, handling edge cases like leading digits, multiple digits, and empty input.
Pro tip: Mention that you would use a StringBuilder (or equivalent) for efficient string concatenation, and discuss the time and space complexity: O(n) time where n is the length of the decoded string, and O(n) space for the output. Also, proactively ask about constraints (e.g., maximum count, input size) to tailor the solution.
Ask the interviewer to confirm the exact format: does the count come before the character? How are multi-digit counts handled? Are there any special characters or escape sequences? This ensures you solve the correct problem.
Explain that you will iterate through the string, building the count digit by digit until a non-digit is encountered, then append the character repeated count times to the result. Use a StringBuilder for efficiency.
Discuss edge cases such as empty input, counts with multiple digits (e.g., '12a'), counts of zero or one, and invalid input (e.g., missing character after count). Decide how to handle them (e.g., throw exception or ignore).
Write clean code with meaningful variable names. Walk through a few examples (e.g., '3a2b' -> 'aaabb', '12a' -> 'aaaaaaaaaaaa') to verify correctness. Mention time and space complexity.
If the interviewer asks for optimization, consider pre-allocating the StringBuilder capacity based on estimated output size, or using a two-pointer approach if the input is very large. Discuss trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked through property-based testing here, generating random strings and checking both directions.
Start by defining the round-trip property precisely: for all inputs, decompress(compress(x)) == x, and for all valid compressed outputs, compress(decompress(y)) == y. Then describe a verification strategy combining unit tests with property-based testing (e.g., random inputs, edge cases) and formal reasoning. Finally, analyze time and space complexity for each function, considering best, average, and worst cases, and discuss trade-offs.
Pro tip: Mention that round-trip verification should include adversarial cases like empty input, maximum-size input, and data with patterns that stress the algorithm (e.g., highly repetitive or random). Also, note that complexity analysis should account for the compressed size, not just the original size, as this often reveals hidden inefficiencies.
Clearly state the two directions: compress then decompress yields the original, and decompress then compress yields the original compressed form (if applicable). Clarify assumptions about input domain and validity of compressed data.
Propose a combination of unit tests for known cases, property-based testing with random and structured inputs, and edge cases (empty, single character, max size). Include fuzzing or differential testing against a reference implementation if available.
Derive the time complexity for compress and decompress as functions of input size (n) and output size (m). Discuss best, average, and worst cases, and note any dependence on data distribution.
Determine auxiliary space used by each function, excluding input and output. Consider in-place vs. out-of-place, and whether the algorithm uses additional data structures like hash tables or buffers.
Highlight trade-offs between time and space, and between compression ratio and speed. Mention potential optimizations like streaming, parallelization, or choosing different algorithms for different data types.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a stack to handle nested brackets: push the current string and repeat count when encountering '[', and pop and repeat when encountering ']'. Iterate through the string, building the result efficiently.
Pro tip: Discuss time and space complexity upfront, and mention that the stack approach is optimal for nested structures. Also, consider edge cases like multi-digit numbers and empty brackets.
Confirm the input format, constraints, and expected output. Ask about edge cases like multi-digit numbers, nested brackets, and invalid inputs.
Decide to use a stack to manage nested contexts. Each stack entry can hold the string built so far and the repeat count.
Iterate through the string: if digit, build number; if '[', push current string and number onto stack and reset; if ']', pop and repeat; if letter, append to current string.
State that time complexity is O(n * maxK) where n is output length and maxK is maximum repeat count, and space complexity is O(n) for the stack and result.
Walk through the given example '3[a2[bc]]' and a few edge cases to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.