How is task complexity assessed?

The complexity assessment of tasks in QA / QA Automation is based on a combination of factors:

• Work volume: The number of test scenarios, functionality to be covered by tests, the scope of regression testing.
• Functionality complexity: How new, complex, or integrated the tested functionality is with other systems.
• Technologies and tools: The team's familiarity with the technologies used, the need to learn new tools for automation.
• Requirements stability: The frequency of changes in the requirements for the functionality.
• Test environment availability: Ease of setting up and maintaining the test environment.
• Dependencies: Dependence on other teams or external systems.
• Risks: Potential risks that could affect task execution (e.g., instability of functionality, data complexity).

The assessment is conducted during the sprint planning stage or before starting work on the task. The following methods may be used:

• Planning Poker: Group estimation using cards, where each participant estimates the task anonymously, then discrepancies are discussed.
• T-shirt Sizing: Assigning sizes (S, M, L, XL) to tasks corresponding to certain effort ranges.
• Story Points: An abstract unit of measurement reflecting the complexity, risk, and uncertainty of the task.
• Expert assessment: Evaluation based on the experience and knowledge of team members or domain experts.

When automating, additional considerations include:

• Automation complexity: How easy or difficult it is to automate test scenarios (e.g., presence of dynamic UI elements, need to work with APIs).
• Framework modification needs: Whether extension or modification of the existing automation framework is required.
• Test data preparation: The complexity of creating and maintaining test data for automated tests.

The assessment is iterative and can be adjusted as the task progresses. It is important for the entire team to participate in the assessment process to obtain more accurate results.