Among all algorithmic challenges that appear in Microsoft’s technical interviews, few are as elegant — and revealing — as the 8-Puzzle problem.
It’s deceptively simple: just slide tiles on a 3×3 grid until they’re in order.
But under the surface, it’s a test of how you model complex state transitions, reason through search spaces, and maintain clarity while coding under time pressure.

💻 Original Problem

8-Puzzle

The 8 Puzzle is a sliding block game played on a 3×3 grid containing 8 numbered tiles (from 1 to 8) and one empty space represented by 0.
The objective is to rearrange the tiles into a specific goal configuration by sliding them one at a time.
Only tiles adjacent to the empty space (0) can be moved, and each move consists of swapping a tile with the 0.

Input Format: 2D array representing the initial state of the puzzle.
Output Format: Integer indicating the minimum number of moves to reach the goal configuration.
If unsolvable, return −1.

Goal State:

1 2 3
4 5 6
7 8 0

Sample Input: [[1, 2, 0], [4, 5, 3], [7, 8, 6]]
Sample Output: 2

🧠 Problem Analysis

At its core, this is a state-space search problem.
Each board configuration represents a node in a graph, and every legal tile move corresponds to an edge connecting two nodes.
The challenge is to find the shortest path from a starting node (the initial configuration) to the goal node (the ordered puzzle).

Because each move has the same cost, Breadth-First Search (BFS) is the natural choice.
BFS expands states layer by layer, guaranteeing that the first time we reach the goal configuration, we’ve found the shortest possible sequence of moves.

In implementation, the 3×3 grid is often flattened into a simple string such as "123456780".
This makes comparison and hashing straightforward.
Each swap of the 0 with a neighboring tile generates a new string — a new node — which is then enqueued for further exploration.

⚙️ Solvability and Optimization

Not every configuration of the 8-Puzzle can be solved.
That’s where the concept of the inversion count comes in.
Flatten the board into a one-dimensional array (ignoring 0).
If the number of inversions is even, the puzzle is solvable; if it’s odd, no sequence of moves will ever reach the goal.

This small mathematical check helps cut off impossible branches early, making the search dramatically more efficient.
Meanwhile, maintaining a visited set ensures no state is processed twice — essential for both correctness and performance.

Since the total number of possible configurations is finite (9! ≈ 362,880), a well-implemented BFS easily handles the problem in practice.

💡 Why BFS Is the Right Approach

Other search strategies — like DFS or even A* — can solve the 8-Puzzle, but in a coding interview, BFS strikes the perfect balance between clarity, optimality, and implementation speed.
It’s easy to reason about, easy to explain, and — most importantly — it guarantees the correct answer.

The real challenge isn’t memorizing code; it’s articulating why BFS works:
how it expands layer by layer, how the queue maintains search order, and why visited-state tracking prevents cycles.
That’s the kind of structured explanation interviewers at Microsoft love to hear.

🚀 The CSOAHelp Advantage

At CSOAHelp, we emphasize not just solving the problem, but explaining it like a professional.
Our real-time interview guidance system helps candidates practice the exact reasoning Microsoft expects:
how to translate a puzzle into a graph, when to pick BFS over DFS, how to reason about time and space complexity, and how to speak confidently while coding.

With live step-by-step coaching and scenario simulation, candidates learn to think out loud, manage time pressure, and stay composed — turning algorithmic intuition into interview success.

With CSOAHelp.com, you don’t walk into the interview alone —
you walk in with structured thinking, clear articulation, and the confidence of real-time preparation.

👉 Visit CSOAHelp.com to learn more.
Every mock, every live coding session, every simulation is one step closer to your dream offer.