testland/model-based-test-graph-author
Build-an-X workflow for model-based testing (MBT) per the canonical definition - authors a state-machine model of the SUT (states + transitions + guards + actions), validates the model is connected and complete, and feeds the model to a test generator (manual / AI / dedicated MBT tool) that produces test paths covering each transition. Per Wikipedia (en.wikipedia.org/wiki/Model-based_testing): MBT "leverages model-based design for designing and possibly executing tests." Use when a complex stateful flow (checkout, onboarding, multi-step wizard) needs systematic coverage that ad-hoc tests miss.
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model-validation-and-coverage.mdreferences/
Model validation and coverage-criteria selection
Deep reference for the model-based-test-graph-author SKILL.md. Consult
when validating a state-machine model or choosing which coverage
criterion drives path generation.
Validate the model
A model with unreachable or dead-end states wastes generation effort. Validate it before generating any paths:
# scripts/validate-model.py
import yaml
model = yaml.safe_load(open('models/checkout.yaml'))
states = {s['id'] for s in model['states']}
initial = next(s['id'] for s in model['states'] if s.get('initial'))
finals = {s['id'] for s in model['states'] if s.get('final')}
# Check 1: every transition references valid states
for t in model['transitions']:
assert t['from'] in states, f"Unknown from-state: {t['from']}"
assert t['to'] in states, f"Unknown to-state: {t['to']}"
# Check 2: every state is reachable from initial
reachable = {initial}
changed = True
while changed:
changed = False
for t in model['transitions']:
if t['from'] in reachable and t['to'] not in reachable:
reachable.add(t['to'])
changed = True
unreachable = states - reachable
assert not unreachable, f"Unreachable states: {unreachable}"
# Check 3: every state can reach a final state
for s in states - finals:
if not can_reach_final(s, model, finals):
print(f"Warning: state {s} cannot reach a final state (deadlock)")
# Check 4: report the counts that bound path generation
print(f"States: {len(states)}; Transitions: {len(model['transitions'])}")
print(f"Possible test paths (transition coverage): {len(model['transitions'])}")The four checks are: (1) every transition references valid states, (2) every state is reachable from the initial state, (3) every non-final state can reach a final state (deadlock guard), and (4) print the state / transition counts.
Generate test paths per criterion
The coverage criterion decides how many paths get generated:
def generate_paths(model, criterion='transition'):
"""Returns list of paths (each a list of transitions)."""
if criterion == 'transition':
# Greedy: walk the graph, prefer untraversed edges
return greedy_transition_cover(model)
elif criterion == 'state':
return paths_visiting_each_state(model)
elif criterion == 'all_pairs':
return all_2_step_pairs(model)
# ...Criteria, from cheapest to most exhaustive:
| Criterion | What it exercises |
|---|---|
transition | every transition at least once |
state | every state visited at least once |
all_pairs | every 2-step transition pair |
all_paths | every path up to length N (rarely viable) |
Why not all-paths
"Because systems can have enormous numbers of possible configurations, finding all paths is impractical. Instead, test criteria are needed to guide the selection of a finite, appropriate number of test cases." (mbt-wiki)
"Model-based testing qualifies as black-box testing since test suites are derived from models and not from source code." (mbt-wiki)
The team picks the coverage criterion (transition, state, all 2-step pairs, all paths up to length N); MBT generates the matching paths.