How is the difficulty of tasks assessed during testing and automation?

The assessment of task complexity in testing and automation processes is most often based on the following factors:

• Functional Area: How complex is the tested function, does it involve critical parts of the system, are there dependencies on other modules.
• Testing Volume: How many test cases need to be developed and executed, what volume of data is required for the test environment.
• Technical Skills: Whether specific knowledge in a certain technology, tool, programming language is required for automation or specific types of testing (e.g., performance).
• Availability of Documentation: How complete and up-to-date is the documentation on functionality, requirements, architecture.
• Environment Stability: How stable is the test environment, are additional configurations or efforts needed to maintain it.
• Risks: What potential risks are associated with testing or automating this task (e.g., high risk of blocking testing due to external dependencies).
• Interaction: Is active interaction with other teams (development, DevOps) or stakeholders required.
• Experience with Similar Tasks: Is there previous experience performing similar tasks in terms of complexity and volume.

Various techniques are used for assessment, such as:

• Planning Poker: The team discusses the task together and estimates its complexity using specially designed cards (often based on Fibonacci numbers).
• T-Shirt Sizing: Estimating complexity by T-shirt sizes (S, M, L, XL), which provides a rougher but quicker estimate.
• Expert Opinion: An experienced team member provides an expert estimate based on their experience.
• Analogy: Estimating complexity by comparing with previously completed similar tasks.

For automation, specific factors are added:

• Locator Complexity: How easy it is to find and stabilize element locators on the page.
• UI Stability: How often the user interface changes.
• Framework Integration: How easily automated scripts can be integrated into the existing test framework.
• Data Preparation: How difficult it is to prepare test data for automated tests.
• Test Reliability: The likelihood of "flaky" tests (unstable tests that sometimes fail without apparent reasons).

Assessing complexity helps in planning resources, setting priorities, forecasting completion time, and managing risks. It is not an exact scientific method but rather a tool for better understanding the scope of work.