code-bert

codeBERT is a package to automatically check if your code documentation is up-to-date. codeBERT currently works for Python code.

code-bert present version is available for Linux and Mac only. We are working on the Windows release. Please hang on

This is CodistAI open source version to easily use the fine tuned model based on our open source MLM code model codeBERT-small-v2

codeBERT-small-v2 is a RoBERTa model, trained using Hugging Face Transformer library and then we have fine tuned the model on the task of predicting the following -

🏆 code-bert output

Given a function body f as a string of code tokens (including special tokens such as indent and dedent) and a doc string d as a string of Natual Language tokens. Predict whether f and d are assciated or not (meaning, whether they represent the same concept or not)

It will produce a report like the following -

run_pipeline -r test_files

 ======== Analysing test_files/test_code_add.py =========


>>> Function "add" with Dcostring """sums two numbers and returns the result"""
>>> Do they match?
Yes

======== Analysing test_files/test_code_add.py =========

>>> Function "return_all_even" with Dcostring """numbers that are not really odd"""
>>> Do they match?
Yes

 ======== Analysing test_files/inner_dir/test_code_get.py =========

>>> Function "get_file" with Dcostring """opens a url"""
>>> Do they match?
No

An example

Let's consider the following code

from pathlib import Path

def get_file(filename):
    """
    opens a url
    """
    if not Path(filename).is_file():
        return None
    return open(filename, "rb")

💡 Using our another open source library tree-hugger it is fairly trivial to get the code and separate out the function body and the docstring with a single API call.

We can use then, the process_code method from this prsent repo to process the code lines in the proper format as codeBERT-small-v2 would want.

Doing the above two steps properly would produce something like the following

• Function - def get file ( filename ) : indent if not path ( filename ) . is file ( ) : indent return none dedent return open ( filename , "rb" ) dedent

• Doc String - opens a url

Ideally then we need some model to run the following Pseudocode

match, confidence = model(function, docstring)

And ideally, in this case, the match should be False

code-bert CLI

The entire code base is built and abvailble for Python3.6+

We have provided very easy to use CLI commands to achieve all these, and at scale. Let's go through that step by step

We strongly recommend using a virtual environment for the followinsg steps

1. First clone this repo - git clone https://github.com/autosoft-dev/code-bert.git && cd code-bert

2. (Assuming you have the virtualenv activated) Then do pip install -r requirements.txt

3. Then install the package with pip install -e .

4. First step is to download and set up the model. If the above steps are done properly then there is command for doing this download_model

5. The model is almost 1.7G in total, so it may take a bit of time before it finishes.

6. Once this is done, you are ready to analyze code. For that we have a CLI option also. Details of that in the following section

---

Assuming that model is downloaded and ready, you can run the following command to analyze one file or a directory containing a bunch of files

usage: run_pipeline [-h] [-f FILE_NAME] [-r RECURSIVE]

optional arguments:
  -h, --help            show this help message and exit
  -f FILE_NAME, --file_name FILE_NAME
                        The name of the file you want to run the pipeline on
  -r RECURSIVE, --recursive RECURSIVE
                        Put the directory if you want to run recursively

So, let's say you have a directory called test_files with some python files in it. This is how you can analyze them

run_pipeline -r test_files

A prompt will appear to confirm the model location. Once you confirm that then the algorithm will take one file at a time and analyze that, recursively on the whole directory.

🏆 It should produce a report like the following -

 ======== Analysing test_files/test_code_add.py =========


>>> Function "add" with Dcostring """sums two numbers and returns the result"""
>>> Do they match?
Yes
******************************************************************
>>> Function "return_all_even" with Dcostring """numbers that are not really odd"""
>>> Do they match?
Yes
******************************************************************

 ======== Analysing test_files/inner_dir/test_code_get.py =========


>>> Function "get_file" with Dcostring """opens a url"""
>>> Do they match?
No
******************************************************************

Stay tuned!