Description
Generate distinct forward edges of a random planted node order, then delegate the actual topological order and cycle check; exact edge count and isolated nodes are retained, and delegated tie-breaking sorts string labels lexicographically.
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.
Generate distinct forward edges of a random planted node order, then delegate the actual topological order and cycle check; exact edge count and isolated nodes are retained, and delegated tie-breaking sorts string labels lexicographically.
When to use this tool
Use this generator to create reproducible dag topology 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 nodeCount, edgeCount 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 nodes, edges, plantedOrder, topologicalOrder, hasCycle. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.
Shuffle a planted vertex order and sample distinct directed edges only from earlier to later positions. Delegate topological sorting and cycle checking and return both the planted and computed orders.1
Delegated operations used for this result: topological sort; cycle checker. Their calculation or parsing conventions apply to the corresponding result fields.2, 3
| Input field | Meaning and units | Accepted input |
|---|---|---|
| nodeCount | Number of vertices, labeled 0 through nodeCount - 1. | Required. Safe integer; minimum 2; maximum 32. |
| edgeCount | Exact number of generated edges; feasibility is checked against the selected construction. | Required. Safe integer; minimum 0; maximum 496. |
| 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 |
|---|---|---|
| nodes | Nodes | Required. Array of Safe integer; minimum 0.; maximum items 32. |
| edges | Edges | Required. Array of Array of Safe integer; minimum 0.; minimum items 2; maximum items 2.; maximum items 496. |
| plantedOrder | Planted Order | Required. Array of Safe integer; minimum 0.; maximum items 32. |
| topologicalOrder | Topological Order | Required. Array of Safe integer; minimum 0.; maximum items 32. |
| hasCycle | Has Cycle | Required. Boolean; exactly false. |
| randomness | Replay source and normalized 32-bit seed, or null seed for an advancing context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional safe-integer 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.
{
"input": {
"nodeCount": 5,
"edgeCount": 5,
"seed": 42
},
"output": {
"nodes": [
0,
1,
2,
3,
4
],
"edges": [
[
0,
3
],
[
0,
4
],
[
1,
3
],
[
2,
1
],
[
4,
3
]
],
"plantedOrder": [
0,
4,
2,
1,
3
],
"topologicalOrder": [
0,
2,
1,
4,
3
],
"hasCycle": false,
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- A valid computed order need not equal plantedOrder and the DAG can be disconnected. Edge count is bounded by nodeCount*(nodeCount-1)/2; no unique ordering is guaranteed.
- A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
- 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
Similar or alternative tools
- Random Dag Longest Path Exercise Generator
Generate bounded weighted DAGs with a guaranteed forward chain, retained topological order and independently replayable longest-path relaxations.
- Strongly Connected Components Finder
Partition a bounded directed graph into canonical strongly connected components and return vertex memberships plus its deduplicated condensation edges using iterative Kosaraju–Sharir searches.