Code documentation writer: how to evaluate result quality
The Code documentation writer tool creates textual descriptions of functions based on your code. You provide the code and platform, and the system returns documentation with parameters, return values, examples and limitations. But how do you know the result is actually useful? In this guide we walk through a concrete example and show how to evaluate accuracy, completeness and suitability of the generated text.
Open tool: Code documentation writer ↗What the tool actually generates
The tool accepts code (up to 8000 characters) and a platform or application name (up to 1000 characters). Output is a text document where each function has its parameters, return value, usage example and possible limitations. This is not executable code nor automatic testing; it is purely textual documentation.
Important: the tool does not analyze your code on the fly; it builds description based on the fragment provided. If the code has ambiguities or lacks comments, the result may be incomplete. Therefore quality evaluation starts by checking whether all functions from the input code appear in the output document.
Input: def add(a, b): return a + b. Output: the add function takes two parameters a and b, returns their sum. Example: add(2, 3) -> 5. Limitations: does not check types; with strings it performs concatenation.
Criterion 1: accuracy of parameters and return values description
Compare each parameter in the generated documentation with what is written in the code. The tool may err in data types, especially if Python lacks annotations. For the add(a, b) function it might assume parameters are numbers, but actually they could be strings or lists. Check that no extra parameters were added and no mandatory ones were skipped.
Separately verify the return value. If a function returns None or nothing, documentation must reflect this. If it returns a complex object, ensure its structure is described correctly. In the add example the return is a sum, but if code returns a tuple the tool should indicate this.
Code: def divide(a, b): return a / b. Documentation may state: returns the quotient of a divided by b. But it will not mention that when b=0 an error occurs. This limitation must be added manually.
Criterion 2: completeness of usage examples
Good documentation contains examples of calling a function with specific arguments and expected result. The tool usually generates one example, but it may be trivial or not reflect real scenarios. Check that the example matches the function signature and contains no errors.
If the function accepts optional parameters or has default values, ensure the example shows their use. Also note if there is an example with boundary values (e.g. empty list, zero, negative number). If such examples are missing, add them yourself.
For the add(a, b) function example add(2, 3) -> 5 is correct, but does not show string operation. One can add: add('a', 'b') -> 'ab', to highlight the lack of type checking.Criterion 3: limitations and warnings
The tool should explicitly state function limitations: unsupported data types, possible errors, side effects. If the generated text has no limitations section, that is a serious shortcoming. Check if mentioned exceptions the function can throw.
For a file-working function it is important to state that it does not create a file if it does not exist, or that a certain format is required. If the tool did not state this, add manually. Remember the tool does not run code, so it may not know about dynamic errors.
Code: def read_file(path): with open(path) as f: return f.read(). Documentation must mention that the function will throw FileNotFoundError if the file does not exist.
Criterion 4: platform and style conformity
You specified a platform or application, e.g. Python 3.8 or Django. The tool may use generic formulations not considering platform specifics. Check if platform-specific types or naming conventions are mentioned.
For Python it is important to state if a function is a generator or coroutine. If the tool does not do this, documentation may mislead. Also pay attention to output language: if you selected Russian, text should be in Russian, without unnecessary anglicisms.
For an async function async def fetch(url): ... documentation must explicitly state that the function is asynchronous and returns a coroutine, not just 'returns data'.
Criterion 5: practical usability and readability
Even accurate documentation can be useless if written in heavy language or not structured. Evaluate if it is easy to find needed information: are there headers, parameter lists, examples highlighted? The tool outputs plain text, so you can edit it to improve readability.
Check for repeating phrases or common words that carry no meaning. Good documentation answers questions: what does the function do, what are its arguments, what does it return, what are limitations, how to use it. If any item is missing, supplement it.
Instead of 'Function performs addition of two numbers' better 'Returns sum of two arguments. Types are not checked, so for strings concatenation occurs.'
Tool limitations and typical errors
The tool does not analyze code dynamically, so it cannot predict behavior for all possible inputs. It also does not check syntax of your code: if the input fragment has an error, documentation may be built on wrong assumptions. Always verify the code you submit is correct.
Furthermore, the tool does not support export to DOCX or PDF, only TXT. It does not save history and has no batch processing. If you need to document many functions, you must do it one by one. Also remember model responses may contain errors, and queue and availability may vary.
If you submitted code with a syntax error, the tool may generate documentation for a non-existent function. Always check the input code.
Evaluating quality of documentation generated by the Code documentation writer tool requires careful check against five criteria: accuracy of parameters and return, completeness of examples, presence of limitations, platform conformity and readability. Use the concrete example with add function to practice. Remember the tool is an assistant, not a replacement for manual analysis. Always check the result and supplement where necessary. Thus you will get documentation that truly helps other developers.
Open tool: Code documentation writer ↗Interface translations are machine-assisted and may need refinement.