Discovering Edge Cases with CodeLogician

One of the most powerful capabilities of CodeLogician is automatic edge-case discovery.

Traditional testing only explores a small number of scenarios. LLM reasoning explores a handful of likely paths.

CodeLogician instead systematically searches the full behavioral space and returns concrete counterexamples when assumptions fail.

Setup

If Claude Code doesn't already know about CodeLogician, start with:

Learn how to use codelogician by running codelogician doc --help

Example: Fee Logic

You have a fee calculation in Python:

def calculate_fee(method: str, amount: float) -> float:
    if method == "card":
        return 0.029 * amount + 0.30
    elif method == "bank_transfer":
        return 0.008 * amount + 1.50
    else:
        return 0.0

Ask Claude Code:

Use CodeLogician to verify that the fee is always less than the transaction amount for positive amounts.

What Happens

Claude Code will:

1. Translate the Python function into an IML model
2. Add a verification goal expressing the property
3. Run codelogician eval check to submit it to ImandraX

Example Counterexample

CodeLogician discovers:

Counterexample found

method = "bank_transfer"
amount = 1.51
fee    = 1.5108

This reveals a real boundary condition where the fixed fee dominates the transaction — a bank transfer of £1.51 costs almost as much in fees as the transfer itself.

Claude Code can then use this finding to suggest a minimum transaction amount or adjust the fee structure.

Why This Matters

Edge cases like this often appear only in production systems.

CodeLogician identifies them automatically by:

• exploring numeric boundaries
• enumerating behavioral regions
• testing logical invariants

No manual test cases needed — the reasoning engine derives them from the code itself.

Next Steps

Continue with:

• Behavioral Decomposition
• Generating Tests