Testing

Testing

Testing Strategy

TaskPilot is tested against normal inputs, invalid inputs, unexpected situations, and failures during AI execution.

The purpose of testing is to verify that the agent produces useful results while handling errors safely.

Test Cases

Normal Input

Provide a valid list of tasks containing descriptions, deadlines, and importance.

Expected result: Tasks are prioritized with reasoning and recommended next actions.

Invalid Input

Provide empty or incorrectly formatted task data.

Expected result: The system detects the problem and provides an appropriate error message.

Missing Information

Provide tasks with incomplete information, such as a missing deadline.

Expected result: The system continues safely and handles the missing information appropriately.

LLM Failure

Simulate or encounter an API/model failure.

Expected result: The error is handled without crashing the application.

Invalid LLM Response

The model may return an unexpected or incorrectly formatted response.

Expected result: The system detects the invalid response and handles it safely.

Repeated Execution

Run the prioritization process multiple times.

Expected result: Each execution completes without corrupting the workflow or state.

Error Handling

TaskPilot separates AI reasoning from deterministic Python validation and execution control. This allows invalid inputs and system failures to be detected before they cause larger problems.

Testing Documentation

Detailed testing information and test scenarios are also available in the project's TESTING.md file.