Writing a high-quality python code is a dream to many, but not many achieve it. Broken python codes are a nightmare for python developers. But how do we audit the quality of python code and define the quality standard of it?
- It should be easy to read and understand.
- It should do what it is meant to do.
- It should be free of bugs.
- It should be easy to maintain as well as extend.
All python developers can simply agree to the determiners as mentioned earlier. To add to that, one weak link in the python code can wreak havoc on your entire project. QAs know how tough it is to list down numerous test cases and yet be away from that faulty code.
As a result, we can’t emphasise more on auditing the quality of a python code. However, before that, let’s clear the doubt that exists regarding code audit vs code review.
Code Audit vs Code Review–What’s the Difference?
You might already know and would have heard about code review. It is simply a process wherein developers review each other’s code to check if the code is written just fine. Code review is done by someone from within the team, and the focus on one part of the code.
In stark contrast, code audit is performed for the whole project by someone outside the team or the organization.
To further give you a line of difference between these two, code review is about testing different codes to check if they are working. On the other hand, a code audit is about givithe python project ng a test drive to someone. Code audit gives the python development project the necessary stimulus by allowing a third-party to test the code. And, the saying, “the more, the merrier” may not be made for the python developers, but it stands relevant for the code to be bug-free.
Code audits are done to secure and meet the current quality standards of the python codes. So, in a way, you can outsource code auditing to another Python development company, while code review can be done in-house by your team.
Want to get your python code audited?
How to Audit the Quality of Python Code?
Our robust approach to python development makes Quixom a leading python development company. The best practices to audit the quality of python code that we share here are based on the real-life code audit.
Let’s check out how to audit the quality of python code using a step-by-step process.
Step 1: Code Repository:
The first step is to examine the code repository. You can look for the version control system to track and offer the modifications to sources such as GIT. You can revisit the code repository to check if it is well-maintained.
Follow the workflow practices of Gitflow. Check for the names of the branches and verify the same. If your project is vast, you can use the appropriate Git tags.
Step 2: Technology Stack:
In the technology stack, you can verify the frontend and backend technologies used for the python development project.
The next step is to check if the components are compatible with each other. To put this into simple terms, Django works the best with the Postgres. As a result, the combination is technologically stronger than any other framework-database used together. If at any step, you find out that the technology stack is not compatible with each other, you might end up in the pool of potential problems.
Step 3: Deployment Configuration:
Well, there is no right or wrong host, you still need to pay close attention to software offering the hosting services such as nginx, gunicorn, uwsgi, and more. You can research more about which hosting services offer what benefits efficiently from undertaking the code audit.
It is crucial to determine whether the application depends on files that are responsible for the virtualisation of the python development environment.
The README file should consist of the following:
- Installation instructions
- Configuration instructions
- User’s manual
- Necessary copyrights and licenses
- Contact details of developers and distributors
- Malfunctions, issues, and bugs that are already found\
- Change log
In addition, maintain an excel sheet with all the necessary passwords and logins to run the application in the required environment.
Step 4: Skip Manual, Adopt Digital:
If you are a python developer, you might have probably heard about PEP 8 style guide and PEP 257 conventions. You don’t have to check for the practices manually. You can use tools to help you get through:
Flake8 can be used to invoke a command line using Python, and it can be called by Git.
Pylint helps in analysing whether the declared interfaces have been implemented in the code. It also checks the code’s length and variable names as per the code standards.
It is used to identify common security issues related to the python code. To detect the security concerns, Bandit can process each of these files and build an AST and run against the AST nodes.
When you run the code audit, the steps mentioned earlier can help you improve your python code’s quality.
How to Get Rid of Lints??
Lints in the python code are inconsistent errors and illogical codes that impact your code’s quality. There is no easy way to detect these lints in the python code. These can be in the form of incorrect tabbing, open bracket, mistyped variable, the wrong set of arguments, and more.
Basically, there are two types of lints:
- Stylistic Lint: When the python code does not conform to the defined set of conventions, it is known as stylistic lint.
- Logical Lint: When there are unpredictable code patterns, unintended results, and errors in the python code, it is known as logical lint.
In order to get rid of the lint and audit the quality of python code, you need to designate the task to linters. Linters can analyse the code and provide better insight into it. There are tools that you can use to prevent lints.
For instance, Flake8 holds the capability to detect logical as well as stylistic lint. It works on the style along with the complexity. In addition, Pylama is a code audit tool that consists of numerous linters and tools to analyze code to check for errors, enforce a coding standard, analyze programs, maintain compliance with conventions, and more.
When Should You Check the Quality of your Python Code?
While working on a python development project, it is crucial for you to pick up on the quality at the right time. Basically, there are no set guidelines for checking the quality of your python code. However, if you believe the experts, there are three most common instances when you can do so:
Data Mining Made Easy
It is no surprise that data is the foreground for automation. In such a case, machine learning algorithms along with data engineering, highly rely on the availability of data. To further leverage the data for the business purpose, it is crucial to exercise data mining. As a result, any flaw arising in the system can be detected and resolved in its initial stage.
Data miners and analysts are realizing the benefits of using Python for businesses and are fast approaching it. Python helps to work with various models of data structures. They can easily upload data in the format of their choice. Besides this, it is even possible to organize these data stacks to make it readable. In addition, Python provides the ability to manipulate data, which can be used to resolve any issue arising in the system at any given point in time.
You have probably read all the python development requirements carefully and formed a logic to write a code. However, being in the coding zone, you might end up diverting from the path. As a result, it is equally important to keep on reviewing your python code while writing it.
This helps in changing the code in the initial phase itself and reduces the time taken during the review and audit of the python code.
Once you and your python development team have completed writing the code, you can review each other’s code to check for the potential errors.
Such practices can help in the identification of the bugs at an early stage of python development for the necessary actions to be taken. This also helps keep the deployment timelines on track.
Once you and your team are done with the project development and the QA has submitted the report, you can opt for python code audit services to help professionals analyze the code from a fresh perspective.
At Quixom, we provide auditing services for your python code and suggest a consistent and logical code to keep the python development project up and running without any bug or error.
When Do you Need to Audit the Quality of your Python Code?
Python development has numerous stages, and you might end up needing the python code audit on various occasions.
Before the Launch:
This one goes without saying. You need to ensure that the quality of your python code is impeccable, which calls for efficient code handling. Since your python software is going to be exposed to masses, you are going to get reviews for the same.
As a result, you need to be 100% sure about the quality of your python code.
Post Inheriting the Legacy Code:
When you have inherited the legacy code and look forward to analyzing the future scope of the project, you need to add-in the auditing of the python code.
Prior to Making Investment in a Project:
There are numerous IT companies offering the best python development services. However, if you are new to the development terms, it is crucial for you to seek a python code audit to ensure that the project that you are investing in is a safe bet to place.
Suboptimal Product Quality:
Feel like your software is lagging behind? It’s time to get your python code the audit it requires. You can check the code and make the necessary changes to it to bring it to optimum quality.
Bonus Section: Top Tips to Audit the Quality of your Python Code
Now that you have read about how, what, and when of auditing the quality of python code, it’s time to get to the checklist.
Before the end users get their hand on the python software, it is crucial that they are tested negative for any precedent or unprecedented errors. Code audit can help make the python code error-free with the following tips to audit the quality of your python code:
- Get at least two developers to review the python code.
- Leverage githooks.
- Choose a specific formatter configuration for the entire team.
- Have timely meetings with your team to conduct knowledge sharing sessions.
- Have a single code editor to be used in the entire team for standardization.
Remember, the key to reducing python development complexity is to work on the extensibility, maintenance, and readability of the code.