Improving Test Efficiency through Data Construction: Practices and Insights

Business background: Complex order status flows across systems A, B, C, and D require extensive data setup, making manual verification costly and time‑consuming.

1. What is data construction? It is the practice of generating test data to improve work efficiency, especially for functional, sandbox, production validation, and performance testing.

2. Common data construction methods are illustrated with diagrams.

3. Our implementation uses the QA‑developed datapt platform, combining a low‑code front‑end configuration with Java back‑end logic, allowing different business teams to write back‑end code and use the front‑end for execution.

4. Key practices include:

Minimize required parameters; auto‑generate when possible.

Prefer simple inputs over complex ones.

Consolidate related business scenarios.

Use selectable options (e.g., dropdowns) instead of manual entry.

Reuse parameters from previous interfaces to avoid duplicate entry.

5. Complex association scenarios are split into manageable contexts, enabling flexible construction of various order flow nodes.

6. Promote broader adoption by sharing updates through meetings, proactive communication, and addressing user pain points.

7. Provide strong after‑sales support by collecting feedback, fixing issues, and building trust with development teams.

8. Pre‑emptive data construction integrates test data creation early in development, offering benefits such as white‑box testing, comprehensive business‑chain coverage, higher code coverage, left‑shift testing, reduced late‑stage data creation challenges, early detection of implementation issues, and reusable scenarios for acceptance testing.

9. Collaborative development involves both QA and developers, ensuring long‑term maintenance and continuous improvement of data construction capabilities.

Conclusion – Value realization:

Supports developer self‑testing, especially front‑end data generation.

Reduces test data creation cost, boosting efficiency.

Enables one‑click verification of upstream/downstream system interactions.

Enhances QA technical skills and gains cross‑department recognition.

Improves regression testing efficiency and code coverage.

Deepens QA understanding of system implementations.

Vision: Leveraging technology to support efficient business operations, so well‑crafted data construction can even let you leave work early.