AI Skill Report Card
Creating Test Strategies
Quick Start8 / 15
Python# Basic test strategy template test_strategy = { "scope": "What are we testing?", "objectives": ["Verify functionality", "Ensure performance", "Check security"], "test_types": ["Unit", "Integration", "System", "Acceptance"], "entry_criteria": ["Code complete", "Environment ready"], "exit_criteria": ["95% pass rate", "No critical bugs"], "resources": ["Tools", "Environment", "Personnel"] }
Recommendation▾
The Quick Start template is too abstract - provide a concrete example like 'Testing login functionality for banking app' with actual test cases instead of generic placeholders
Workflow12 / 15
Progress:
- Define test scope and objectives
- Identify test types and levels
- Create test cases and scenarios
- Set up test environment
- Execute tests and track results
- Report findings and recommendations
Step-by-Step Process
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Scope Definition
- Identify what needs testing (features, systems, processes)
- Define boundaries (what's included/excluded)
- Document assumptions and constraints
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Test Planning
- Choose appropriate test types (functional, non-functional)
- Define test levels (unit, integration, system, acceptance)
- Establish entry/exit criteria
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Test Case Design
- Write detailed test scenarios
- Include positive and negative test cases
- Cover edge cases and error conditions
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Execution Strategy
- Set up test environment
- Execute tests systematically
- Log defects and track progress
Recommendation▾
Examples need actual input/output pairs - show specific test scenarios with expected results rather than bullet point lists
Examples12 / 20
Example 1: Web Application Testing Input: E-commerce checkout process Output:
- Test login functionality
- Verify cart operations (add/remove items)
- Test payment processing
- Check order confirmation
- Validate error handling
Example 2: API Testing Strategy Input: REST API for user management Output:
- Authentication tests (valid/invalid tokens)
- CRUD operations (Create, Read, Update, Delete)
- Input validation (boundary values, invalid data)
- Response validation (status codes, data format)
- Performance testing (load, stress)
Recommendation▾
Add concrete templates or frameworks (like BDD scenarios, test case formats, or risk assessment matrices) instead of just listing concepts
Best Practices
- Risk-based testing: Prioritize high-risk areas
- Traceability: Link test cases to requirements
- Automation: Automate repetitive tests
- Documentation: Maintain clear test artifacts
- Continuous improvement: Learn from each cycle
Common Pitfalls
- Testing too late in development cycle
- Insufficient test data or environment setup
- Focusing only on happy path scenarios
- Not testing error conditions and edge cases
- Poor communication of test results
- Skipping regression testing after changes