UAT Workflow Guide

The UAT (User Acceptance Testing) workflow automates the end-to-end process of inspecting a study, generating a test specification, producing synthetic test records, submitting them to the API, and monitoring the asynchronous jobs until completion.

Visual Workflow

The following diagram illustrates the internal orchestration logic of the UAT workflow:

sequenceDiagram participant User participant Orchestrator as UATWorkflow participant Inspector as StudySchemaInspector participant Builder as UATSpecificationBuilder participant Generator as SyntheticRecordGenerator participant Submitter as BulkRecordSubmissionWorkflow participant Poller as PollingManager User->>Orchestrator: run("STUDY_KEY") Orchestrator->>Inspector: inspect("STUDY_KEY") Inspector-->>Orchestrator: StudySnapshot Orchestrator->>Builder: build(StudySnapshot) Builder-->>Orchestrator: UATSpecification Orchestrator->>Generator: generate(UATSpecification, StudySnapshot) Generator-->>Orchestrator: List[GeneratedRecordSet] Orchestrator->>Submitter: submit("STUDY_KEY", List[GeneratedRecordSet]) Submitter-->>Orchestrator: SubmissionResult (with Job IDs) Orchestrator->>Poller: monitor(Job IDs) Poller-->>Orchestrator: UATRunResult (Pass/Fail) Orchestrator-->>User: UATRunResult

Usage Example

The following code demonstrates how to use the UATWorkflow to automate test validation:

from imednet import ImednetSDK, load_config
from imednet_workflows.uat import UATWorkflow


def main():
    try:
        cfg = load_config()
    except ValueError as e:
        print(f"Skipping execution: {e}")
        return

    # In a real environment, you'd execute against an active study.
    # Here we show how to instantiate and run the workflow.
    try:
        with ImednetSDK(
            api_key=cfg.api_key,
            security_key=cfg.security_key,
            base_url=cfg.base_url,
        ) as sdk:
            UATWorkflow(sdk)
            # Inspects study, builds spec, generates synthetic data, submits, and monitors.
            # Replace "MY_STUDY" with a real study key.
            # result = workflow.run("MY_STUDY")
            # print(result.summary())
    except Exception as e:
        print(f"Failed: {e}")


if __name__ == "__main__":
    main()

Detailed Steps

1. Inspection: The workflow starts by fetching the current study design (forms, variables, sites) to build a StudySnapshot.

2. Specification Building: A specification is created that maps out exactly which tests (e.g., REGISTER_SUBJECT, CREATE_NEW_RECORD) need to be run against which forms.

3. Generation: Synthetic data payloads are generated to fulfill the specification.

4. Submission: The data is pushed to the API using the bulk submission workflow.

5. Monitoring: Finally, the orchestrator polls the job endpoints until all batches are either COMPLETED or FAILED, generating a detailed summary.