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A code review is a process that applies various checks and tests to ensure that code is high quality before it’s merged. During a code review, senior developers lend a second pair of eyes to code, giving developers guidance from a more experienced counterpart.
A code reviewer looks for ways to improve code across several areas:
Bugs: Code reviews can uncover bugs so they can be remediated before going into production.
Readability/maintainability: Code reviews are an opportunity to enforce style guides to ensure developers can collaborate and understand each others’ work.
Efficiency: Code reviews involve checks to ensure code will run quickly and efficiently.
Security: Code reviews ensure code is free of high-risk vulnerabilities.
Code reviews initially happened manually, but a variety of tools exist to help developers automate the review process. In this post, we’ll cover the types of code reviews, the role of static analysis, our top eight Python code review tools, and a checklist to help you execute your own code reviews.
There are several types of code reviews, including manual, unit testing, and linting. Let’s take a quick look at each one:
In this type of review, the process is manual, with a review team coordinating the steps from start to finish. Virtual collaboration tools that allow developers to see and share work can help streamline manual review tasks.
Unit testing works on the principle that it’s much easier to correct a small portion of code than a large section of code. Unit testing is built into Python and allows you to analyze code in the smallest testable chunks. Developers can confirm that code sections work as expected before merging them with the remainder of the code.
Linting is a type of code review that uses a linting tool to scan code to uncover potential bugs. Since linters scan code without running it, they are considered a simple type of static analysis tool.
Static analysis tools do much of the heavy lifting that would normally require careful line-by-checks during a code review. These quickly grow cumbersome for longer, more complex projects.
As mentioned above, linters are an example of static analysis tools that serve this purpose.
An open source that is widely used in the Python community, pylint is highly configurable so you can customize your pre-commit checks. For instance, you can customize the confidence level a warning requires to be displayed, or configure pylint to show suggestions instead of false positive-prone error messages.
2. Snyk Code
Snyk Code is a static analysis tool that uncovers vulnerabilities within code before it goes into production, ensuring that the code you’re reviewing is secure, and you don’t pass any security vulnerabilities down the development pipeline.
GitHub has a built-in code review tool that performs lightweight code reviews for every pull request. GitHub includes other features that help with reviews, including diffs, history, and blame view that give you various insights into the changes a piece of code has undergone.
CodeScene uses machine learning to identify areas of poor code health and offers suggestions for improvement. It differs from other tools in its behavioral analysis component, which gives insights into how teams of developers collaborate. This helps you understand the factors contributing to technical debt.
Collaborator is a code review tool designed for companies with hefty regulatory requirements. It features a comprehensive tool for reviews along with detailed reports and electronic signatures for regulatory compliance.
Codacy is an automated code review tool that works with Python (among more than 40 programming languages). It is noted for its easy GitHub integration, its sharing features that establish a single source of truth, and its open source and startup friendly principles.
RhodeCode unifies security and code review across repositories including Git, Subversion, and Mercurial. Large-scale, global organizations that use multiple repositories can benefit from its coding and collaboration features. An on-premises version is available by download. A cloud-hosted instance is available for enterprises.
Reviewable is a GitHub tool that gives richer features than what is built into GitHub. These include a commenting feature that requires all threads to be completed before merging, and a feature that blocks multiple commits from a single pull request.
As we’ve seen, there are a plethora of tools that approach Python code review with different tactics. Despite the convenience of these tools, none of them are smarter than a competent code review team. Here are some best practices your team should follow to conduct better Python code reviews:
Code reviews don’t exist in a vacuum. Instead, they should start with your project requirements. Being clear about these can ensure your code addresses a specific problem.
Modern development is typically a team practice so it’s important to ensure that code is also easy to read and understand. This helps other developers maintain code in case its author moves on from the organization. PEP 8 is a widely used Python style guide (written by authors including Guido himself!) that’s intended to help Python developers write more readable code.
It's important to follow protocol around pull requests to help teammates work together as they review code. GitHub has its own recommendations for pull requests, such as including the purpose of the pull request and specifications about feedback. These serve as a good template for your own protocol.
Does your code actually work? Verifying that code meets your requirements is a critical step in any review. Furthermore, code should be checked to ensure that it will integrate well with the larger project.
With the arrival of agile development methodologies and a shift left approach to security, testing should occur early and often within the development process. Developers should also apply secure coding practices to avoid issues from the start.
One of the key factors for code quality is maintainability, a subjective quality around how easy it is for someone other than the author to maintain or update code. To help other developers understand what code does, it’s important to use documentation. This can include self-documentation, where the code itself is simple enough to understand on its own, or docstrings after a definition of a function or class for more complex code.
For more tips, check out our post on code review best practices.
Why should you use a code review tool for Python?
Developers love to automate tasks as much as possible, and code review is no different. Code review tools make it possible to review large-scale projects with dozens of developers. They allow you to test code without running it, and ensure you can be confident before merging the pull request.
How is a Python code review done?
A Python code review starts with being clear about the requirements for the code. It then checks if the requirements are satisfied (ie: the code actually works), along with other considerations such as readability, security, and documentation. The overall process is manual but a plethora of tools exist to help development teams as they conduct code reviews.
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