What the data for this category looks like
The data sources for this category are defect management systems, test reports, code commit records, and user feedback. The data update frequency is daily or per version. Daily produced documents include defect tickets, regression checklists, repair verification records, and defect statistical reports. Defect tickets include defect number, severity level, reproduction environment, associated test case ID, repair status, and associated version number. Regression checklists include case number, execution status, execution time, and executor. Repair verification records include verified cases, verification results, and verification time. This document is written based on FastGPT v4.17.0.
What constraints these characteristics impose on the "workflow orchestration" link
Dispersed data sources require the workflow to support API nodes connected to multiple systems, to synchronize defect and test data from different channels. High data update frequency requires that the workflow's trigger cycle be flexibly configured for daily or per-version triggers, to avoid repeatedly processing unchanged data. Diverse document structures and varying fields require the workflow's variables configuration to support custom extraction of exclusive fields for different documents, ensuring accurate data flow. The most time-consuming links are defect reproduction, priority judgment, and regression verification. The workflow must support configurable parallel execution nodes and timeout handling logic, to prevent single links from blocking the entire process. For serious risks such as high-priority defect omissions or version delays, the workflow must be configured with failure alerts and retry mechanisms to reduce exception risks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Parallel task limit | 6 | Adapt to conventional resource limits for local deployments, avoid exceeding system carrying capacity |
| Node timeout | 600 seconds | Match the average time spent on defect reproduction and regression verification, avoid unnecessary timeouts |
| Failure retry count | 2 | Prevent workflow interruptions caused by temporary network fluctuations or short system freezes |
variables configuration | Customize variables based on the fields of defect tickets and regression checklists | Adapt to field extraction needs for multiple document types, ensure accurate data flow |
| Workflow trigger timing | Per version release or daily early morning | Match data update frequency, avoid repeatedly processing unchanged data |
nodeId configuration | Specify core node IDs | Ensure the workflow execution path follows preset logic, avoid incorrect node execution order |
Three common configuration errors
- Phenomenon: When the workflow runs, a maximum of 6 nodes load simultaneously. Any additional nodes trigger a loading timeout status displayed on the interface. Cause: The parallel task limit configuration was not adjusted, and the default value restricts concurrent execution count.
- Phenomenon: Calling an API to trigger the workflow returns a
504status code. Cause: A reasonable node timeout was not configured, exceeding the system's default timeout threshold. - Phenomenon: Extracted defect ticket fields in the workflow do not match the source document content. Cause: The field mapping rules in the
variablesconfiguration do not match the actual document fields, leading to incorrect data extraction.
How to confirm the configuration is correct
- View the workflow run logs to confirm that the parallel task count matches the configured limit.
- Submit test defect tickets or regression checklist data, trigger the workflow, and check that the extracted fields match the source document content.
- Simulate a node timeout scenario to confirm that the workflow executes the failure retry logic as configured.
- Verify the workflow trigger timing to confirm it matches the data update cycle.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.