Automated Docstring Generation For Python Funct...

If you use the auto-generation features of MkDocs together with mkdocstrings, then you can create good documentation with less effort. Start your documentation with docstrings in your code, then build it into a deployed and user-friendly online resource that documents your Python project.

Automated Docstring Generation for Python Funct...

The package called mkdocstrings-python is the Python handler for mkdocstrings that allows mkdocstrings to parse Python code. You installed it by adding the extension [python] when installing the mkdocstrings package with pip.

I find this task interesting and would like to find out more about how to contribute: for example, would fine-tuning for PL to NL (code comment/docstring) generation for Python be a suitable case for this project?

Good programmers prefer to write the logic behind each class, methods, function etc using comments. In python, the best way to add the comments for the functions or class is to write in docstring format. Later, it can be used by documentation automation tools like sphnix to generate the document. But for the automation, you should follow the standard docstring guideline. Here is the small example of good docstring example,

Using the automated documentation feature of Sphinx, you can make withease the extensive documentation of Python program.You just write python function documents (docstrings), Sphinxorganizes them into the document, can be converted to a variety offormats.In this session, I'll explain a documentation procedure that uses withsphinx autodoc and autosummary extensions.

Sphinx provides autodoc feature that generate document from docstringin your python sources.The docstring that contains description and example of the use offunction written near the program, makes doc easy to update.In addition, the output of the Sphinx will make you understand what towrite in docstring. As a result, this will improve your motivation ofdoc writing.

robotpy-build will find doxygen documentation comments on many types of elementsand use sphinxify to translate them into python docstrings. All elements thatsupport documentation strings can have their docstrings set explicitly usinga doc value in the YAML file.

Various mature automated test generation tools exist for statically typed programming languages such as Java. Automatically generating unit tests for dynamically typed programming languages such as Python, however, is substantially more difficult due to the dynamic nature of these languages as well as the lack of type information. Our Pynguin framework provides automated unit test generation for Python. In this paper, we extend our previous work on Pynguin to support more aspects of the Python language, and by studying a larger variety of well-established state of the art test-generation algorithms, namely DynaMOSA, MIO, and MOSA. Furthermore, we improved our Pynguin tool to generate regression assertions, whose quality we also evaluate. Our experiments confirm that evolutionary algorithms can outperform random test generation also in the context of Python, and similar to the Java world, DynaMOSA yields the highest coverage results. However, our results also demonstrate that there are still fundamental remaining issues, such as inferring type information for code without this information, currently limiting the effectiveness of test generation for Python.

Automated unit test generation is an established field in research and a technique well-received by researchers and practitioners to support programmers. Mature research prototypes exist, implementing test-generation approaches such as feedback-directed random generation (Pacheco et al. 2007) or evolutionary algorithms (Campos et al. 2018). These techniques enable the automated generation of unit tests for statically typed programming languages, such as Java, and remove the burden of the potentially tedious task of writing unit tests from the programmer.

We introduce our automated test-generation framework for Python, called Pynguin, in the following sections. We start with a general overview on Pynguin. Afterwards, we formally introduce a representation for the test-generation problem using evolutionary algorithms in Python. We also discuss different components and operators that we use.

We use our Pynguin test-generation framework to empirically study automated unit test generation in Python. A crucial metric to measure the quality of a test suite is the coverage value it achieves; a test suite cannot reveal any faults in parts of the subject under test (SUT) that are not executed at all. Therefore, achieving high coverage is an essential property of a good test suite.

Overall, the results we achieved from our experiments indicate that automated test generation for Python is feasible. They also show that there exists a ranking between the performance of the studied algorithms, which is in line with previous research on test generation in Java (Campos et al. 2018).

To the best of our knowledge, little has been done in the area of automated test generation for dynamically typed languages. Search-based test generation tools have been implemented before, for example, for the Lua programming language (Wibowo et al. 2015) or Ruby (Mairhofer et al. 2011). While these approaches utilise a genetic algorithm, they are only evaluated on small data sets and do not provide comparisons between different test-generation techniques.

Further tools are, for example, AugerFootnote 17, CrossHairFootnote 18, or KlaraFootnote 19; they all require manual effort by the developer to create test cases in contrast to our automated generation approach.

In this work, we presented Pynguin, an automated unit test generation framework for Python. We extended our previous work (Lukasczyk et al. 2020) by incorporating the DynaMOSA, MIO, and algorithms, and by evaluating the achieved coverage on a larger corpus of Python modules. Our experiments demonstrate that Pynguin is able to emit unit tests for Python that cover large parts of existing code bases. In line with prior research in the domain of Java unit test generation, our evaluation results show that DynaMOSA performs best in terms of branch coverage, followed by MIO, MOSA, and the Whole Suite approach.

Install required packages:pip install mkdocs==1.3.0 mkdocstrings==0.18.1Instead of directly adding these requirements to our requirements.txt file, we're going to isolate it from our core required libraries. We want to do this because not everyone will need to create documentation as it's not a core machine learning operation (training, inference, etc.). We'll tweak our setup.py script to make this possible.We'll define these packages under a docs_packages object:# setup.pydocs_packages = [ "mkdocs==1.3.0", "mkdocstrings==0.18.1"]and then we'll add this to setup() object in the script:123456789# Define our packagesetup( ... install_requires=[required_packages], extras_require= "dev": docs_packages, "docs": docs_packages, ,)Now we can install this package with:python3 -m pip install -e ".[docs]"We're also defining a dev option which we'll update over the course so that developers can install all required and extra packages in one call, instead of calling each extra required packages one at a time.python3 -m pip install -e ".[dev]"We created an explicit doc option because a user will want to only download the documentation packages to generate documentation (none of the other packages will be required). We'll see this in action when we use CI/CD workflows to autogenerate documentation via GitHub Actions.

alphadoc is a python standalone utility tool that automatically generates docstrings to help document the code better in python. It renders a customizable docstring template after each method detected in the code. Along with this, it also supports common and widely used docstrings formats such as Numpy, Google, ReStructured Text, and Epytext (Javadoc).

It must also be noted that the Napolean project, which facilitates the automated generation of HTML documentation-pages from Python docstrings, has extended the Google specification to match the sections of the NumPy style. The Napolean version of the NumPy and Google docstring styles can be found here.This resource also includes useful examples of docstrings for modules and classes.

The bindings for each block exist in blockname_python.cc under the python/bindings directory. Additionally, a template header file for each block that is used as a placeholder for the scraped docstrings lives in the docstrings/ dir

If Doxygen is enabled in GNU Radio and/or the OOT, Docstrings are scraped from the header files, and placed in auto-generated [blockname]_pydoc.h files in the build directory on compile. Generated templates (via the binding steps described above) are placed in the python/bindings/docstrings directory and are used as placeholders for the scraped strings

blockname_pydoc.h is generated during compilation based on the template in the docstring directory. When the block is first created in blocktool, this template does not exist. Run gr_modtool bind inside build/gnuradio-runtime/python/gnuradio/gr to generate the appropriate template used as a placeholder for the scraped docstrings

The pydoc module is used toview or generate HTML documentation from the docstrings in a script or module.It can be executed using the pydoc command (or, equivalently,python -m pydoc), followed by the name of the module or script file.If the script is in the current directory, use either its module name (ie itsfile name without the .py extension) or prefix its filename with ./ (or .\on Windows).By default, pydoc will display the documentation on the command-line.To generate HTML files, use the -w option.

python-docstring is a minor mode for intelligently reformatting (refilling) and highlighting Python docstrings. It understands both epytext and Sphinx formats (even intermingled!), so it knows how to reflow them correctly. It will also highlight markup in your docstrings, including epytext and reStructuredText. 041b061a72