7 Best Python Code Review Tools Recommended by Developers

With software development teams growing in size, many developers utilize static code analysis tools to mitigate code smells and find bugs and vulnerabilities. While code correctness cannot be automated, code quality maintenance can be made possible through these static code analysis tools. It helps Teams to identify bugs and anti-patterns in the early stage of development.

Static code analysis tools analyze the code on every commit or pull request and point out potential code-quality, security, and style issues before the reviewer points them out. In this article, we will be reviewing 7 of the renowned Python code review tools as your perfect fit, which will make your decision-making process easier for choosing the best one.

Let's get started.

Why use Code Review Tools for Python?

Python is one of the most flexible languages for Development. With this advantage, the Developers tend to misuse the flexibility, which violates the standard practices. Some of the common issues that we face while maintaining code quality include:

• Imported but unused modules
• Missing arguments in function calls
• Unnecessary use of comprehensions
• Lack of proper indentation
• Lack of whitespace around parentheses, brackets, or braces

It affects the code's readability and makes code review difficult.

In most cases, it is taken care of by using static analysis tools like PyLint or Flake8. These tools are prone to a lot of noise and false positives that make code reviews hard.

Code review tools can cut many pain points. It can identify code complexity and help us find and fix problematic code. With the tool's findings, the code violations can be kept in check, thus keeping the review process faster and efficient.

1. DeepSource

DeepSource provides static code analysis against various general-purpose programming languages like Python, Javascript, Golang, and more. DeepSource provides customization and the ease with which it allows us to perform static analysis on the Python code. DeepSource generates a Configuration File, embedded in the repository, and the code is continuously analyzed.

Some of the features of DeepSource include:

• Single-file configuration for continuous analysis
• Support for code formatters like Black and AutoPep8
• Quality checks on every pull request
• Automated fixes for common Issues
• Embed in CI/CD pipelines like Travis CI to test coverage

DeepSource provides a low false-positive rate and resolution time, compared to various other static code analysis tools. DeepSource provides access to framework-related issues that makes it easy for maintainers to review.

Besides that, DeepSource also makes it easy to operate on private repositories. With every pull request or commit, a private token is used to fetch the code. The analysis is then run in an isolated environment. Once the analysis is complete, the codebase is purged, mitigating risks of security violations.

2. Codacy

Codacy is another tool that provides code review and code coverage reports for various general-purpose programming languages like Python. Codacy covers a wide variety of issues like code coverage, duplication, complexity, and more. It helps the developers to keep the code quality intact and the code review clean.

Some of the features of Codacy includes:

• Automation of code review
• Analyze the code quality over time.
• Automated resource suggestions to help developers.
• Takes into account only new issues to prevent noise.
• Analyzes pull requests and commit individually.

Some of the drawbacks are:

• Lacks of issues priority that should help developers to put more focus.
• Lack of feature to export code patterns.
• Setting configuration pages is hard.

However, Codacy suffers from a compounded setup that requires a lot of configuration and a high false-positive rate.

3. SonarQube

SonarQube provides continuous inspection of code quality to perform automatic reviews. Its Static Code Analysis Tool can smell bugs, anti-patterns, and even security vulnerabilities in Python. SonarQube is also easy to pair with a CI/CD pipeline for effective code quality management.

SonarQube implements the Code Analysis functionality through two of its tools. The Sonar Scanner allows the implementation of the analysis, which goes back to the SonarQube Server, where the results are managed and persisted.

Some of the features of SonarQube includes:

• Identification of tricky Issues like security vulnerabilities and execution paths bugs.
• Provides access to webhooks and APIs to automate the code review process.
• Enforces a quality gate as per the requirements and the practices
• Provides plugins for various popular IDEs, thus mitigating the need for the complete package.

Some of the drawbacks are:

• Lacks the ability to set automated analysis and alerts.
• Lacks feature to ignore issues that should not be fixed.

Setting up SonarQube for a Python Project is tricky because it requires installing packages and plugins to set up the client analysis and server storage. You can check out the official documentation to know more about SonarQube configuration for a Python project.

4. Veracode

Veracode is yet another popular code review Tool for Python. It not only provides scanning for common vulnerabilities and exposures, but its static analysis can also identify issues that make it easy to report bugs and anti-patterns. Veracode also provides other offerings through its enterprise service, which includes interactive analysis and dynamic analysis.

Some of the key features include:

• Provides developer tools, API and workflow integration to make code quality checks easier.
• Seamless integration with DevOps pipelines.
• SCA agent-based scanning for finding issues and vulnerabilities.
• Keeps libraries and licensing in sync with PyPi.
• Forwards a risk Score with each scan.

Some of the drawbacks are:

• Lacks easier Continuous Integration setups
• Lack of an intuitive User-Experience

Installing and setting up the Veracode agent-based scan agent is relatively easy. You can use Python's standard package manager pip to install the tool and start the code analysis. However, Veracode lacks features to optimize the scanning and lacks language-specific recommendations.

5. Checkmarx

Checkmarx is an application security testing and static code analysis tool. It provides features like static application testing, runtime, and interactive testing, including dependency scanning that allows easy scanning of source code and ironing out vulnerabilities.

Some of the key features include:

• Uses Checkmarx SAST for static analysis and finding security vulnerabilities
• Provides integration with CI/CD pipelines.
• Intuitive and easy to use user-interface
• Provides plugins for various popular IDEs

Some of the drawbacks are;

• High false-positive rate.
• Scanning during continuous integration takes a lot of time.

The best part about Checkmarx is that it offers native support for most general-purpose programming languages with no configuration required. Checkmarx, however, faces issues with false Positive rate and a lack of support towards large codebases.

6. Coverity

Coverity is a static analysis tool that aims to find and fix defects in many popular general-purpose programming languages like Python, Javascript, Ruby, Java, and more. It aims to test and scan every line of the Code and has been adopted by many companies like Boeing, Lockheed Martin, and more.

Some of the key features include:

• Tests possible execution path to reduce false positives
• Easy to set up and customize making it flexible for development needs
• Provides a setup wizard to specify Python paths.
• Easily integrated with GitHub, Jenkins, and Travis CI workflows.

Some of the drawbacks are:

• Licensing is dependent on the number of lines of code analyzed.
• Overpriced compared to similar products in the market.

Coverity, however, suffers from a high turnaround time when the codebases are large and complex. Due to the inherent complexity, the false positives need to be handled carefully.

7. CodeScene

CodeScene is not only a static code analysis tool but also provides behavioral analysis, allowing developers to identify patterns as their codebase evolves. CodeScene can be invoked through a Git service provider like Github or Bitbucket or used on-premise.

Some of the key features include:

• Analyze your version control history and produce visualizations.
• Enable developers to find bugs and issues.
• Uses machine learning algorithms to find patterns.
• Makes code quality subjectable to business impact.

Some of the drawbacks are:

• Lack of an intuitive user-interface.
• Difficult for users to follow up with improvements.

CodeScene enables developers to find the bugs and issues that can plague their code with a layer of attention to identify and fix them. CodeScene can also measure the technical risk of a developer leaving the team and identifies productivity bottlenecks.