Refactor Without Fear: Differential Testing with AI-Generated Inputs

#ai #testing #refactoring #python

Most developers have seen a refactor fail in production. Tests pass. Production breaks. The cause is always an edge case nobody wrote.

Differential testing catches that class of failure. Run two implementations on the same inputs. Compare every output. If they differ, something broke.

This article shows a practical workflow. It uses MonkeyCode's free model access and free server. The model generates boundary inputs. The server runs the comparison.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Why AI-Generated Inputs?

Hand-written tests are written by the same people who write the refactor. They share blind spots. AI models propose unusual values. Empty strings. Negative numbers. Unicode. Nonexistent dates.

A single prompt can produce hundreds of inputs. That covers more ground than most manual test tables.

The Differential Harness

Here is a minimal Python script. It calls a model to generate inputs. Then it executes a legacy and a refactored function on each input.

import json
import subprocess
from typing import Callable

def generate_test_inputs(prompt: str) -> list:
    """Call MonkeyCode's model endpoint. Returns a JSON list."""
    # Replace with your actual client code.
    cmd = ["monkeycode", "generate", "--prompt", prompt]
    raw = subprocess.check_output(cmd, text=True)
    return json.loads(raw)

def differential_test(legacy: Callable, refactored: Callable, inputs: list) -> list:
    failures = []
    for value in inputs:
        try:
            old = legacy(value)
        except Exception as e:
            old = f"ERROR:{type(e).__name__}"
        try:
            new = refactored(value)
        except Exception as e:
            new = f"ERROR:{type(e).__name__}"
        if old != new:
            failures.append({"input": value, "legacy": old, "refactored": new})
    return failures

# Example: date formatting
def legacy_date(s: str) -> str:
    parts = s.split("-")
    return f"{parts[2]}/{parts[1]}/{parts[0]}"

def refactored_date(s: str) -> str:
    import datetime
    try:
        d = datetime.date.fromisoformat(s)
        return d.strftime("%d/%m/%Y")
    except ValueError:
        return "invalid"

prompt = (
    "Generate 50 JSON strings that are date-like boundaries: "
    "leap days, month 00, month 13, day 32, missing parts, "
    "empty string, non-string values, timestamps. Return a JSON array."
)
inputs = generate_test_inputs(prompt)
failures = differential_test(legacy_date, refactored_date, inputs)
print(json.dumps(failures, indent=2))

The harness records exceptions as strings. A function that raises KeyError instead of ValueError is a difference. That is valuable information.

Running It on MonkeyCode's Free Server

The free server executes scripts without tying up a developer machine. Package the harness and run it after every refactor. The 10 million token grant covers many batches.

Schedule it with a cron job. Or trigger it from a pull request webhook. The server environment is disposable, so failed runs do not corrupt anything.

Limitations

Differential testing only proves consistency. Two implementations can both be wrong. The test passes, and the bug stays.

AI-generated inputs need validation. Models may return duplicates or malformed values. Filtering is mandatory.

Model output changes between runs. Different inputs, different coverage. Store the generated inputs in a file to make runs reproducible.

Who Should Not Use This

Teams with formal verification requirements need property-based testing or proof assistants. Financial systems with strict audit trails may not accept model-generated inputs.

Free token quotas are not a compliance guarantee. Use differential testing as a safety net, not a certificate.

The Takeaway

Refactoring is scary because of unknown unknowns. Differential testing with AI inputs makes the unknown visible. MonkeyCode provides the model and the server for free. The only cost is writing a small harness.

Try it on your next risky refactor. The February 29 bug does not stand a chance.

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