Random Graph Reachability Mutation Corpus Generator

Description

Apply controlled insertions or deletions to a supplied simple directed graph and retain exact reachable sets before and after every mutation.

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.

Apply controlled insertions or deletions to a supplied simple directed graph and retain exact reachable sets before and after every mutation.

When to use this tool

Use this generator to create reproducible graph reachability mutation corpus 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 nodes, edges, start, operation, count 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 initialEdges, finalEdges, steps. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Validate a simple directed graph and repeatedly sample an eligible addition or existing-edge deletion. Recompute BFS reachability before and after every mutation and return gained and lost node lists.^[1]

Delegated operations used for this result: breadth first traversal. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
nodes	Nodes	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 16.
edges	Edges	Required. Array of Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 2.; minimum items 0; maximum items 64.
start	Start	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
operation	Operation	Required. Text; one of "insert", "delete".
count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 1; maximum 8.
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
initialEdges	Initial Edges	Required. Array of Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 2.; minimum items 0; maximum items 64.
finalEdges	Final Edges	Required. Array of Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 2.; minimum items 0; maximum items 72.
steps	Steps	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 8.
steps[].edge	Edge	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 2.
steps[].before	Before	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 16.
steps[].after	After	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 16.
steps[].gained	Gained	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 0; maximum items 16.
steps[].lost	Lost	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 0; maximum items 16.
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": {
    "nodes": [
      "a",
      "b",
      "c"
    ],
    "edges": [
      [
        "a",
        "b"
      ],
      [
        "b",
        "c"
      ]
    ],
    "start": "a",
    "operation": "delete",
    "count": 1,
    "seed": 42
  },
  "output": {
    "initialEdges": [
      [
        "a",
        "b"
      ],
      [
        "b",
        "c"
      ]
    ],
    "finalEdges": [
      [
        "a",
        "b"
      ]
    ],
    "steps": [
      {
        "edge": [
          "b",
          "c"
        ],
        "before": [
          "a",
          "b",
          "c"
        ],
        "after": [
          "a",
          "b"
        ],
        "gained": [],
        "lost": [
          "c"
        ]
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

A mutation can leave reachability unchanged. Additions exclude self-loops and repeated edges, and deletions remove the selected directed edge only; request count must fit available mutations.
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 Graph Reachability Mutation Corpus Generator — tool.io
Calculation or validation: graph/breadth-first-traversal — tool.io

Similar or alternative tools

Random Graph Articulation Point Exercise Generator
Construct candidate cut-vertex exercises and verify each vertex removal using connected-component counts, with bounded optional chords.
Random Graph Bridge Cut Exercise Generator
Construct a recursive connected graph with optional chords and label candidate/all-edge removals by delegated component changes.