Test matrices and runs
Test matrices define what an application should detect. Test runs submit what the application actually detected. Fictix scores the submitted findings against planted truth.
Base path:
/api/v1/demo-jobs/{job_ref}
Create test matrix
POST
/api/v1/demo-jobs/{job_ref}/test-matricesPOST /api/v1/demo-jobs/{job_ref}/test-matrices
Example body:
{
"name": "Accounting QA",
"scenarios": [
{
"scenario_id": "ghost-employee",
"severity": "critical",
"required": true
}
]
}
Use a matrix to define the expected detection contract for a product workflow.
List test matrices
GET
/api/v1/demo-jobs/{job_ref}/test-matricesGET /api/v1/demo-jobs/{job_ref}/test-matrices
Get test matrix
GET
/api/v1/demo-jobs/{job_ref}/test-matrices/{matrix_id}GET /api/v1/demo-jobs/{job_ref}/test-matrices/{matrix_id}
Delete test matrix
DELETE
/api/v1/demo-jobs/{job_ref}/test-matrices/{matrix_id}DELETE /api/v1/demo-jobs/{job_ref}/test-matrices/{matrix_id}
Create test run
POST
/api/v1/demo-jobs/{job_ref}/test-runsPOST /api/v1/demo-jobs/{job_ref}/test-runs
Example body:
{
"matrix_id": "tm_abc123",
"detected": [
{
"entity_type": "employee",
"entity_id": "emp_ghost_001",
"finding": "ghost employee"
}
]
}
Fictix compares submitted findings with the matrix and scenario truth.
List test runs
GET
/api/v1/demo-jobs/{job_ref}/test-runsGET /api/v1/demo-jobs/{job_ref}/test-runs
Get test run
GET
/api/v1/demo-jobs/{job_ref}/test-runs/{run_id}GET /api/v1/demo-jobs/{job_ref}/test-runs/{run_id}
Evaluation pattern
- Create a demo job.
- Inject or generate scenarios.
- Fetch the scenario manifest.
- Load the company into the target application.
- Run the target application or agent.
- Convert the target application's findings into Fictix detected findings.
- Submit a test run.
- Store the score and verdict.
Detected finding shape
Detected findings should be specific enough to match planted truth:
{
"entity_type": "vendor",
"entity_id": "ven_123",
"finding": "duplicate vendor"
}
Avoid vague findings such as:
{
"finding": "something is wrong"
}
Specific findings produce better scoring and better user-facing verdicts.
