Random Solvable Maze Test Case Generator

Description

Generate a bounded four-neighbor binary grid by independent wall draws followed by carving a shuffled monotone start–goal path, then verify a shortest path with the existing A* solver and serialize the grid; distribution is this construction, not uniform solvable grids, perfect mazes, or unique solutions.

A graph uses vertices and edges to represent relationships. Direction, duplicate handling, indexing, and traversal order affect its interpretation; returning a constructed graph together with a witness or calculated answer helps verify an algorithm.

When to use this tool

Use this generator to create reproducible solvable maze test case cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply rows, columns, start, goal, wallProbability in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read grid, start, goal, plantedPath, solution, gridJson. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Draw a binary wall grid and shuffle the horizontal and vertical steps of a monotone start-to-goal path. Clear that planted path, then cross-check it with non-diagonal A* and return the grid and shortest-path answer.^[1]

Delegated operations used for this result: a star; as json. Their calculation or parsing conventions apply to the corresponding result fields.^{[2, 3]}

Input field	Meaning and units	Accepted input
rows	Rows	Required. Safe integer; minimum 1; maximum 32.
columns	Columns	Required. Safe integer; minimum 1; maximum 32.
start	Start	Required. Object; no unknown keys.
start.x	X	Required. Safe integer; minimum 0; maximum 31.
start.y	Y	Required. Safe integer; minimum 0; maximum 31.
goal	Goal	Required. Object; no unknown keys.
goal.x	X	Required. Safe integer; minimum 0; maximum 31.
goal.y	Y	Required. Safe integer; minimum 0; maximum 31.
wallProbability	Wall Probability	Required. Finite number; minimum 0; maximum 1.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
grid	Grid	Required. Array of Array of Safe integer; one of 0, 1.; minimum items 1; maximum items 32.; minimum items 1; maximum items 32.
start	Start	Required. Object; no unknown keys.
start.x	X	Required. Safe integer; minimum 0; maximum 31.
start.y	Y	Required. Safe integer; minimum 0; maximum 31.
goal	Goal	Required. Object; no unknown keys.
goal.x	X	Required. Safe integer; minimum 0; maximum 31.
goal.y	Y	Required. Safe integer; minimum 0; maximum 31.
plantedPath	Planted Path	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 63.
plantedPath[].x	X	Required. Safe integer; minimum 0; maximum 31.
plantedPath[].y	Y	Required. Safe integer; minimum 0; maximum 31.
solution	Solution	Required. Object; no unknown keys.
solution.cost	Cost	Required. Safe integer; minimum 0; maximum 62.
solution.explored	Explored	Required. Safe integer; minimum 1.
solution.path	Path	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 63.
solution.path[].x	X	Required. Safe integer; minimum 0; maximum 31.
solution.path[].y	Y	Required. Safe integer; minimum 0; maximum 31.
gridJson	Grid Json	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 4096.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "rows": 3,
    "columns": 4,
    "start": {
      "x": 0,
      "y": 0
    },
    "goal": {
      "x": 3,
      "y": 2
    },
    "wallProbability": 1,
    "seed": 42
  },
  "output": {
    "grid": [
      [
        0,
        0,
        1,
        1
      ],
      [
        1,
        0,
        0,
        0
      ],
      [
        1,
        1,
        1,
        0
      ]
    ],
    "start": {
      "x": 0,
      "y": 0
    },
    "goal": {
      "x": 3,
      "y": 2
    },
    "plantedPath": [
      {
        "x": 0,
        "y": 0
      },
      {
        "x": 1,
        "y": 0
      },
      {
        "x": 1,
        "y": 1
      },
      {
        "x": 2,
        "y": 1
      },
      {
        "x": 3,
        "y": 1
      },
      {
        "x": 3,
        "y": 2
      }
    ],
    "solution": {
      "cost": 5,
      "explored": 6,
      "path": [
        {
          "x": 0,
          "y": 0
        },
        {
          "x": 1,
          "y": 0
        },
        {
          "x": 1,
          "y": 1
        },
        {
          "x": 2,
          "y": 1
        },
        {
          "x": 3,
          "y": 1
        },
        {
          "x": 3,
          "y": 2
        }
      ]
    },
    "gridJson": "[[0,0,1,1],[1,0,0,0],[1,1,1,0]]",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Coordinates are zero-based and must lie inside the grid. Clearing the path changes the final wall frequency from wallProbability. The solvable maze is not uniformly sampled and may have multiple shortest routes.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Solvable Maze Test Case Generator — tool.io
Calculation or validation: pathfinding/a-star — tool.io
Calculation or validation: matrix/as-json — tool.io

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