Vanilla Claude vs GitAuto: Test Generation Compared

We ran an experiment. Take a simple Python calculator - 40 lines of code, four arithmetic operations, and a CLI main function. Give it to vanilla Claude with a generic prompt, then give the same file to GitAuto. Compare the results.

Both use the same Claude Opus 4.6 model. The difference is in the system around it - the prompts, the pipeline, and the adversarial testing approach.

The Source Code

def add(a, b):
    return a + b

def subtract(a, b):
    return a - b

def multiply(a, b):
    return a * b

def divide(a, b):
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

def main():
    print("Simple Calculator")
    print("Operations: +, -, *, /")
    a = float(input("Enter first number: "))
    op = input("Enter operation (+, -, *, /): ")
    b = float(input("Enter second number: "))
    operations = {"+": add, "-": subtract, "*": multiply, "/": divide}
    if op not in operations:
        print(f"Unknown operation: {op}")
        return
    result = operations[op](a, b)
    print(f"{a} {op} {b} = {result}")

Vanilla Claude: "Write Tests for This"

We pasted this into Claude Opus 4.6 with a generic prompt and asked it to write unit tests. It produced 19 tests:

• 5 tests for add (positive, negative, mixed signs, floats with pytest.approx, zeros)
• 4 tests for subtract (positive, negative result, negative numbers, floats)
• 5 tests for multiply (positive, by zero, negative, mixed signs, floats)
• 5 tests for divide (positive, float result, negative, mixed signs, divide by zero)

19 well-written tests. Clean structure, good use of pytest.approx for floats, covers the happy paths and the one explicit error case. But notice what's missing: no main() tests, no infinity, no duck typing, no type mismatches, no boundary values.

GitAuto: 41 Tests

GitAuto generated 41 tests for the same file (PR #10). Both handle float precision correctly with pytest.approx - that's table stakes. The difference is in the categories vanilla Claude skipped entirely:

Infinity and NaN

def test_infinity(self):
    assert add(float("inf"), 1) == float("inf")

def test_inf_minus_inf(self):
    assert math.isnan(add(float("inf"), float("-inf")))

float("inf") is a valid Python value. In 1982, the Vancouver Stock Exchange lost half its index value because nobody tested how repeated float operations accumulate. These tests verify behavior with values most developers never think to pass.

Duck Typing and Type Mismatches

def test_string_concatenation(self):
    assert add("hello", " world") == "hello world"

def test_type_mismatch_raises(self):
    with pytest.raises(TypeError):
        add(1, "two")

In December 2025, Cloudflare's Lua proxy went down for 25 minutes because a nil value appeared where an object was expected - a type exploit in a dynamic language. These tests document what add actually does with strings and mixed types, so you know before production does.

Division Boundaries and Main Function

def test_very_small_divisor(self):
    result = divide(1, 1e-300)
    assert result == pytest.approx(1e300)

def test_invalid_first_number(self, _mock_print, _mock_input):
    with pytest.raises(ValueError):
        main()

Dividing by 1e-300 produces 1e300 - a valid but astronomically large result. And vanilla Claude never tested main() at all - no invalid inputs, no empty operators, no error paths. GitAuto generated 9 tests for main() covering all branches.

The Numbers

The Fair Criticism

Could you close this gap with a better prompt? Partially. Asking Claude to "test edge cases, type coercion, and boundary values" would get you closer. The gap isn't about a secret prompt - it's about doing this automatically across hundreds of files without writing a prompt for each one. On a 14-repo codebase, we took statement coverage from 40% to 70% over 7 months using this approach. No developer wrote a single test prompt.

Why This Matters

Basic tests catch bugs you already thought about. Adversarial tests catch bugs you didn't - the kind that took down the Vancouver Stock Exchange, Bitcoin, and Cloudflare. The gap between 19 and 41 tests on a calculator becomes the gap between 40% and 70% coverage on a real codebase.

Read more about what adversarial tests are, try guessing what tests a calculator needs, or estimate the savings for your team with the ROI calculator.