AI makes tech debt more expensive

  # Given a data set of size `size' >= 0, and a `text` string describing
  # the subset size, return a 2-element tuple containing a text string
  # describing the complement size and the actual size as an integer. The
  # text string can be in one of four forms (after stripping leading and
  # trailing whitespace):
  #
  #  1) the empty string, in which case return ("", 0)
  #  2) a stringified integer, like "123", where 0 <= n <= size, in
  #   which case return (str(size-int(n)), size-int(n))
  #  3) a stringified decimal value like "0.25" where 0 <= x <= 1.0, in
  #   which case compute the complement string as str(1 - x) and
  #   the complement size as size - (int(x * size)). Exponential
  #   notation is not supported, only numbers like "3.0", ".4", and "3.14"
  #  4) a stringified fraction value like "1/3", where 0 <= x <= 1,
  #   in which case compute the complement string and value as #3
  #   but using a fraction instead of a decimal. Note that "1/2" of
  #   51 must return ("1/2", 26), not ("1/2", 25).
  #
  # Otherwise, return ("error", -1)

  def get_complement(text: str, size: int) -> tuple[str, int]:
    ...

  get_complement("1/2", 100) == ("1/2", 50)
  get_complement("0.6", 100) == ("0.4", 40)
  get_complement("100", 100) == ("0", 0)
  get_complement("0/1", 100) == ("1/1", 100)

get_complement("0.8158557553804697", 448_525_430): this tests the underlying system uses decimal.Decimal rather than a float, because float64 ends up on a 0.5 boundary and applies round-half-even resulting in a different value than the true decimal calculation, which does not end up with a 0.5. (The value is "365932053.4999999857944710")

get_complement("nan", 100): this is a valid decimal.Decimal but not allowed by the spec.

get_complement("1/0", 100): handle division-by-zero in fractions.Fraction

get_complement("0.", 100): this tests that the string complement is "1." or "1.0" and not "1"

get_complement("0.999999999999999", 100): this tests the complement is "0.000000000000001" and not "1E-15".

get_complement("0.5E0", 100): test that decimal parsing isn't simply done by decimal.Decimal(size) wrapped in an exception handler.

Also, this isn't the full spec. The real code reports parse errors (like recognizing the "1/" is an incomplete fraction) and if the value is out of range it uses the range boundary (so "-0.4" for input is treated as "0.0" and the complement is "1.0"), along with an error flag so the GUI can display the error message appropriately.

Also with your example above I probably would break the function down into smaller parts, for two reasons 1) you can more easily unit test the components; 2) generally I find AI performs better with more focused problems.

So I would probably first write a signature like this:

  # input examples = "1/2" "100" "0.6" "0.99999" "0.5E0" "nan"
  def string_ratio_to_decimal(text: str) -> number

Sure. Internally I have multiple functions. Though I don't like unit testing below the public API as it inhibits refactoring and gives false coverage feedback, so all my tests go through the main API.

> Pasting that into Claude, without any other context

The context is the important part. Like the context which says "0.5E0" and "nan" are specifically not supported, and how the calculations need to use decimal arithmetic, not IEEE 754 float64.

Also, the hard part is generating the complement with correct formatting, not parsing float-or-fraction, which is first-year CS assignment.

> # Handle special values

Python and C accept "Infinity" as an alternative to "Inf". The correct way is to defer to the underlying system then check if the returned value is infinite or a NaN. Which is what will happen here because when those string checks fail, and the check for "/" fails, it will correctly process through float().

Yes, this section isn't needed.

> # Handle empty string

My spec says the empty string is not an error.

> numerator, denominator = text.split("/"); num = float(numerator); den = float(denominator)

This allows "1.2/3.4" and "inf/nan", which were not in the input examples and therefore support for them should be interpreted as accidental scope creep.

They were also not part of the test suite, which means the tests cannot distinguish between these two clearly different implementations:

  num = float(numerator)
  den = float(denominator)

  num = int(numerator)
  den = int(denominator)

    if not isinstance(text, str):
        return None
    
    # Remove whitespace
    text = text.strip()
    
    # Handle empty string
    if not text:
        return None

    # Handle ratio format (e.g., "1/2")
    if "/" in text:
        try:
            numerator, denominator = text.split("/")
            num = int(numerator)
            den = int(denominator)
            if den == 0:
                return float("inf") if num > 0 else float("-inf") if num < 0 else float("nan")
            return num / den
        except ValueError:
            return None

    # Handle regular numbers (inf, nan, scientific notation, etc.)
    try:
        return float(text)
    except ValueError:
        return None