Edge Case Testing

Edge case testing is where AI assistance becomes particularly valuable. Humans often miss edge cases because we think in happy paths; AI has been trained on code that handles the unhappy paths too.

Common Edge Cases AI Identifies

Input boundaries:

• Empty values (null, undefined, "", [], )
• Maximum/minimum values
• Type mismatches
• Unicode and special characters

State conditions:

• Concurrent access
• Race conditions
• Partial failures
• Network interruptions

Data scenarios:

• Duplicate entries
• Missing required fields
• Circular references
• Extremely large datasets

Prompting for Edge Cases

Basic prompt: "What edge cases should I test for this function?"

Better prompt: "Analyze this function and list:

1. Input edge cases it might not handle
2. State conditions that could cause failures
3. Integration points that might break
4. Security-related edge cases"

AI-Generated Test Patterns

AI can generate test code for identified edge cases:

"Generate tests for the edge cases you identified. 
Use [testing framework]. 
Include both the test and expected behavior."

When to Focus on Edge Cases

• Before merging any AI-generated code
• For functions handling user input
• For code processing external data
• For anything security or payment related

Edge case testing transforms AI's broad pattern knowledge into concrete protection for your specific code.