Test protection evidence schema¶

This page documents the protection payloads returned by Python and exposed by ordeal audit --json. Values are JSON-compatible unless noted.

MutationResult.test_protection_view¶

result = ordeal.mutate("myapp.scoring.compute", preset="standard")
view = result.test_protection_view()

Field	Type	Meaning
`status`	`str`	`weak`, `inconclusive`, or `protective_within_measured_scope`
`protects`	`bool \| null`	Scoped Boolean decision; null when inconclusive
`summary`	`str`	Human-readable reason for the verdict
`mutation_score`	`str \| null`	Exact `killed/total (percent)` text
`surviving_mutants`	`int`	Tested mutants that escaped
`kill_attribution`	`list[object]`	Killer, kill count, and operator names
`property_strength`	`list[object]`	Observations and kills per property
`tautological_or_weak_properties`	`list[str]`	Exercised properties with zero kills
`unexercised_properties`	`list[str]`	Declared properties with zero observations

Each kill-attribution row has test, kills, and operators. Property killers use the prefix property:.

Each property-strength row has name, holds, total, mutants_killed, mutants_tested, and status.

ModuleAudit.test_protection_view¶

audit = ordeal.audit("myapp.scoring")
view = audit.test_protection_view()

This view combines the migrated coverage measurement with aggregated mutation validation.

Field	Type	Meaning
`label`	`str`	`resulting test protection`
`status`	`str`	Combined verdict
`protects`	`bool \| null`	Scoped decision
`summary`	`str`	Decisive evidence in one sentence
`mutation_score`	`str \| null`	Aggregated score text
`killed_mutants`	`int`	Aggregated killed count
`tested_mutants`	`int`	Aggregated tested count
`surviving_mutants`	`int`	Aggregated survivors
`kill_attribution`	`list[object]`	Weakest observed killers
`line_coverage_percent`	`float \| null`	Verified migrated line coverage
`coverage_gaps`	`list[int]`	Exact missing executable lines
`coverage_gap_count`	`int`	Missing statement count
`property_strength`	`list[object]`	Rows include their mutation target
`tautological_or_weak_properties`	`list[object]`	Full weak-property rows
`unexercised_properties`	`list[object]`	Full unexercised-property rows

The Python mutation view returns property names in its two convenience lists; the module audit view returns full rows because it aggregates multiple targets.

CLI JSON path¶

ordeal audit myapp.scoring --json

The envelope includes:

raw_details.protection_views[]

Each row adds module to the ModuleAudit.test_protection_view() fields. The same module serialization also contains evidence_views.test_protection.

Decision order¶

For a mutation result:

any survivor → weak;
any unexercised declared property → weak;
no tested mutants → inconclusive;
otherwise → protective_within_measured_scope.

For a module audit, surviving mutants win first, then unexercised properties, coverage gaps, missing mutation evidence, and unverified coverage. Only complete measured evidence reaches protective_within_measured_scope.

Interpretation boundaries¶

Mutation score depends on target, preset, filters, and available operators.
Equivalent-mutant filtering reduces noise but cannot solve equivalence in all cases.
Coverage gaps are executable line gaps, not a complete branch-coverage model.
Kill attribution identifies observed killers, not every redundant test.
A scoped protective verdict is evidence, not a universal correctness proof.