Being leading diversified team for years. Besides Software Architect for more than 13 years for several different type of projects. I use to review several pull requests each day.
Sometimes it is tremendously time consuming. Especially when you have to tackle the not clear commit messages, SCRUM bug reports or work log are not clearly defined the issues or features.
Here is an example of an ML code review with a pull request from GitHub:
Title: Add a new model to the librar
Author: John Doe
Reviewer: Jane Doe
Description:
This pull request adds a new model to the library. The model is a simple linear regression model that can be used to predict house prices.
Changes:
* Added the new model to the library.
* Added unit tests for the new model.
* Updated the documentation to include the new model.
Review:
I have reviewed the pull request and I have found no major issues. The code is well-written and the unit tests are comprehensive. I recommend that the pull request be approved.
Approve/Reject:
Approved
Here are some additional things to keep in mind when reviewing ML code:
- Make sure that the code is well-documented. The documentation should be clear and concise, and it should explain how the code works.
- Test the code thoroughly. The unit tests should be comprehensive and they should cover all possible scenarios.
- Consider the impact of the changes. Think about how the changes will affect other parts of the code.
- Be constructive in your feedback. The goal of a code review is to help the author improve the code, not to criticize them.
Here are some tips on how to review pull requests:
- Start by reading the commit messages. The commit messages should provide a clear and concise overview of the changes that have been made.
- Run the code. This is the most important step in the review process. You should run the code to make sure that it compiles and runs without errors.
- Check for style violations. The code should follow the project's style guide. You can use a linter to help you check for style violations.
- Test the code. You should write unit tests to test the code. This will help you to make sure that the code works as expected.
- Review the documentation. If the code includes documentation, you should review the documentation to make sure that it is accurate and up-to-date.
- Leave comments. If you find any problems with the code, you should leave comments on the pull request. This will help the author to fix the problems.
- Approve or reject the pull request. Once you have reviewed the pull request, you should approve or reject it. If you approve the pull request, the code will be merged into the main branch. If you reject the pull request, the author will need to make changes to the code before it can be approved.
- Be constructive in your feedback. The goal of a pull request review is to help the author improve the code, not to criticize them.
- Be specific. Don't just say "the code is bad." Explain what is wrong with the code and how it can be improved.
- Be helpful. If you can, offer suggestions on how the code can be improved.
- Be timely. Don't leave pull requests hanging for days or weeks.
There are a few ways to make the review test automatic as much as possible for eg. Python.
- Use a linter. A linter is a tool that can be used to check for style violations in Python code. There are many different linters available, such as flake8 and pylint.
- Write unit tests. Unit tests are small, isolated tests that can be used to verify the functionality of a piece of code. Unit tests can be run automatically, which can help to make the review process more efficient.
- Use a continuous integration (CI) server. A CI server is a tool that can be used to run unit tests and other automated checks on code changes. CI servers can be configured to run automatically whenever a new pull request is submitted.
Here are some specific tools and libraries that you can use to automate the review test for Python:
- Flake8: A linter that can be used to check for style violations in Python code.
- Pylint: A linter that can be used to check for style violations and potential bugs in Python code.
- Unittest: A library that can be used to write unit tests for Python code.
- Travis CI: A CI server that can be used to run unit tests and other automated checks on code changes.
- CircleCI: Another CI server that can be used to run unit tests and other automated checks on code changes.
