Why Is Python the Best Language for Improving Quality Analysis of Your Software?

Python can have an infinite number of benefits when it comes to software testing. It automates multiple mundane tasks and facilitates faster testing on a vast number of parameters. With proper scripts, analyzing the quality of your application can happen much conveniently, thus, requiring the least amount of human intervention.

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Last Updated on by Jitendra Chaturvedi

Why Is Python the Best Language for Improving Quality Analysis of Your Software?
Why Is Python the Best Language for Improving Quality Analysis of Your Software?

Why Is Python the Best Language for Improving Quality Analysis of Your Software?

Why Is Python the Best Language for Improving Quality Analysis of Your Software? Python is one of the most popular programming languages of the world. Not only is it the top choice for development and machine learning among organization and researchers, but also in domains like software testing. While Guido van Rossum first created Python, seldom did he know that the language would be used by as many as 8 million people around the world. But, with Python’s growing popularity and ease of use, it is finding applications in several other fields for good.

One such relatively new field where Python is finding applications is the quality analysis of software. With recent advancements in technology, more and more companies these days are using Python scripts for their testing their software and finding any bugs. On the one hand, it has replaced manual testing to a great extent, while on the other, helped companies easily find difficult bugs in large scale projects, that too in the least amount of time.

Be it Python’s ease of understanding, flexible implementation, or anything else. Organizations all over the world are leaving behind traditional approaches to software testing and adapting Python scripts for efficient quality analysis. Analyzing the quality of your application is as crucial as developing it. You never know what the outcome might be unless you test an application under robust conditions. This practice determines your position in the market as well as among your clients. The healthier your testing process is, the better will you find bugs and other elements that can be fixed or improved to make your application much better in quality.

Python can have an infinite number of benefits when it comes to software testing. It automates multiple mundane tasks and facilitates faster testing on a vast number of parameters. With proper scripts, analyzing the quality of your application can happen much conveniently, thus, requiring the least amount of human intervention.

Manual vs Automated QA

Even though automating everything sounds picture-perfect, it is not the case. We can’t just run scripts on the computer and rely on thoroughly analyzing the quality of the application. It is imperative to remember that applications and software are designed for humans, and we interact with them in several ways. One of the most critical bugs is found when the interaction of a user with an application takes place. While automation can do a lot of things, it fails to analyze software in term of human interaction. For this reason, manual testing cannot be called dead, at least yet.

Running an automated test on application software, again and again, will not tell you if something has changed or impacted the usability. This makes automation of very little use as compared to human thinking and judgement. Therefore, until AI reaches its pinnacle where it is entirely able to replace programmers, etc., it is challenging to replace manual testing with automation.

On the other hand, if we take a look at the market in recent years, a lot has changed significantly. Large scale and complex projects are raising new challenges for testers. As a result, quality analysts now need to work on a broad set of configurations and devices. In such scenarios, automation can be of great help by helping handle repetitive tasks along with performance and load tests. Therefore, modern-day testers must have both coding abilities and manual testing skills so that they can combine them wisely and put to use.

Different suitable languages for QA

Today we have a plethora of programming languages in the world, each with its own set of benefits and applications, Be it C, C++, C#, Ruby or Python, all of these are great technologies. While some of these are more suited towards analyzing the quality of application software, others are not.

Software testers can use any of these languages for testing, and it won’t have much effect on the outcome when combined with manual testing. But, the only challenge is learning and implementing scripts in these languages. For this reason, Python is a preferred language among testers and quality analysts, since it is easy to learn and implement.

Qualities of Python that make it the best language for QA

Python is one of the most natural languages to learn, which is yet another reason why more than 8 million people across the world use it. When it comes to quality analysis and software testing, Python has more than a few advantages. Let’s take a look at them-

  • Readable Code: Python’s syntax is crystal clear and looks almost like English. It is due to its readability and scripting nature that Python makes an excellent programming language.
  • General Purpose Programming language: One of the best qualities of Python is that it is a general-purpose programming language. This means it can be used to solve almost every programming problem, including web applications, desktop applications, data analysis, scripting, task automation and quality analysis, among others.
  • Rich Libraries: The abundance of libraries in Python is one of its greatest virtues. It helps in efficiently accomplishing the desired task without having to code everything from scratch. For example, with the ‘unit test’ module in Python, one can quickly provide all the test inputs at once and analyze the entire application. The module has both a built-in testing framework and test runner. Similarly, another module called the Pytest module is one of the most popular testing modules used in Python for quality analysis of applications.
  • Coding productivity: Python increases the coding productivity with the least amount of code required to solve big problems. As a result, it saves time while designing scrips to test applications.
  • Script Execution: Python also comes pre-installed on systems like Linux and Mac, which is why you can smoothly run Python script from the shell on Linux servers.

Conclusion

The need for manual testing will exist as long as programmes are coded by people. With technologies like Python, testing can surely get fast and more time-saving. You can hire python developers in India to become more productive when testing large scale applications. The key lies in implementing python scripts for testing, without any delays.

— The article is written by Vijay Chauhan.

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2 Comments
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