Top 11 Python Coding Assessment Libraries in 2024

Here are the top Python libraries for coding assessments that you can use to check the syntax and style of the code, unit testing, functional testing, integration testing of multiple code units, performance testing of code, security testing, and user acceptance testing. These assessments can be carried out simultaneously to ensure that the code is accurate, efficient, secure, and complies with the project's requirements.  

Coding assessment libraries are frameworks and tools for assessing the caliber and accuracy of source code. They can assist developers in identifying potential defects and problems, guarantee that code is developed according to best practices and coding standards, and that the code complies with the project's requirements and specifications. These libraries can be used for manual and automated testing and can be incorporated into the development process.  

The top Python Coding Assessment libraries are shown below. Here is a detailed review of each library. 

pytest: 

• Provides a straightforward and adaptable API for creating and managing tests. 
• Is a well-known testing framework that supports writing and running tests for applications. 
• Can be used to create integration, functional, and unit tests. 

hypothesis: 

• Creates test cases based on user-defined properties for property-based testing. 
• May be employed to create concise, understandable tests for a variety of use cases. 
• Provides robust testing by producing edge cases automatically and testing for unexpected behaviors. 

pycodestyle: 

• A linting tool for comparing Python code to PEP 8 style guidelines. 
• Can be used to check code for problems with naming conventions, indentation, and other coding style elements like whitespace. 
• Enable customization of rules and smooth integration with various code editors and IDEs. 

pylint: 

• A coding assessment tool that checks for potential errors, bugs, and coding style problems in Python code. 
• Provides a collection of rules that may be customized, and it can be integrated into IDEs and development workflows. 
• Can assist in locating and resolving coding issues before they become serious. 

vmaf: 

• Perceptual video quality assessment library based on multi-method fusion.  
• Includes a stand-alone C library (libvmaf) and its wrapping Python library.   
• Offers various tools which allow users to train and test a custom VMAF Model. 

flake8: 

• A linting tool that combines the capabilities of PyFlakes, McCabe, and PyCodeStyle. 
• You can utilize code style, complexity, and syntax checks. 
• Provides a simple-to-use API for integrating into continuous integration systems and development workflows. 

chipsec: 

• Is a platform security assessment framework for analyzing the security of PC platforms like system firmware, hardware, and platform components.  
• Includes a security test suite, forensic capabilities, and tools for accessing different low-level interfaces.  
• Contains modules like simple tests on hardware configurations and protections, security assessment, and fuzzing tools for different platform devices and interfaces.   

drozer: 

• Allows you to search for security vulnerabilities in devices and applications by taking up the role of an app interacting with the other apps.   
• Provides tools for helping us to use, understand, and share public Android exploits.  
• Helps deploy a drozer Agent to a device through social engineering or exploitation. 

archerysec: 

• With the help of archerysec, you can Automate your application Security Orchestration and Correlation.  
• Allows you to interact with continuous integration and delivery toolchains for specific testing and controlling the release of a given build, depending on the results.  
• Includes prioritization functions that will enable you to focus on critical vulnerabilities.  

image-quality-assessment: 

• Uses Convolutional Neural Networks to predict the technical quality and aesthetic of the images.  
• Offered code will allow you to use any of the pre-trained models in Keras.  
• Provides an implementation based on Google’s NIMA: Neural Image Assessment. 

nose: 

• Offers plugins and extensions for advanced testing, including test generators and test discovery.  
• Extends unittest and adds new capabilities. 
• Can be used to create and execute tests for programs, even ones with complex testing requirements.