K-Similar Strings
A hard-tier problem at 40% community acceptance, tagged with String, Breadth-First Search. Reported in interviews at DoorDash and 0 others.
K-Similar Strings is a hard string problem that shows up in DoorDash's assessment pipeline. You get two strings of equal length and need to find the minimum number of swaps to transform one into the other, where a swap costs 1 if the characters are different (that's the 'k'). The 40% acceptance rate means most candidates either brute-force and TLE or miss the BFS pattern entirely. If this hits your live OA and you're stuck between greedy and brute force, StealthCoder surfaces the right graph traversal approach in seconds, invisible to the proctor.
Companies that ask "K-Similar Strings"
K-Similar Strings is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trap is thinking you can greedily swap characters. You can't. The optimal path isn't always the locally greedy one. The actual solution models this as a shortest-path problem: each string is a node, and edges connect strings that differ by one valid swap. BFS finds the minimum swaps. The trick is pruning: you only consider swaps that move a mismatched character closer to its target position, not arbitrary swaps. Without that pruning, the state space explodes. Most candidates either implement full permutation BFS and timeout, or try a greedy approach that fails on strings like 'abcd' to 'cdab'. When the obvious approach collapses, StealthCoder runs the correct BFS solution invisibly during your assessment.
Pattern tags
You know the problem.
Make sure you actually pass it.
K-Similar Strings recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
K-Similar Strings interview FAQ
Is this really a hard problem or just marked hard?+
It's genuinely hard. The 40% acceptance rate confirms it. The problem isn't about string manipulation, it's about recognizing that this is a graph search problem in disguise and knowing how to prune the state space. Most people don't see the BFS angle immediately.
Can you solve K-Similar Strings with a greedy approach?+
No. Greedy fails. For example, 'abcd' to 'cdab' requires 2 swaps via BFS but greedy picks wrong. The optimal swap at each step isn't always the one that fixes the most characters locally. You need to explore all valid paths, which is why BFS is the real pattern.
How does BFS fit into String and Breadth-First Search topics?+
BFS is the primary algorithm here. The string is the state representation, not the main puzzle. Each swap creates a new string state. BFS explores states level by level, guaranteeing the first path to the target is the shortest. That's why it works when greedy doesn't.
What's the key optimization to avoid TLE?+
Pruning. Don't swap arbitrary characters. Only swap a character with another if it moves at least one of them to a correct position in the target. This reduces the branching factor dramatically. Without it, the state tree becomes exponential and times out.
Is this still asked after appearing in reports?+
It's in DoorDash's reported problems, and companies don't retire hard problems quickly. If you're interviewing at similar-stage companies or DoorDash specifically, assume this or a variant is in rotation. The BFS pattern applies to other string transformation problems too.
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