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Analyzing the quality of your software is crucial to any business. The process appears towards the end of your software development lifecycle but indeed decides the fate of it. In other words, quality analysis demonstrates a process in which the actual output of the software is tested with its expected output. There are a variety of test inputs that are used in the process of quality analysis so that the product sheds light on the actual truth of where it stands.
The aspect of business growth is fundamental to quality testing of the products even if it doesn’t appear that way. When you build a product, there is more than one developer involved in the job who has their style of coding. As multiple modules are combined, there is a strong possibility that there might present a lot of bugs in the outcome. If you hand over the product to your client or sell it to your customers this way, chances are they will chase you for their money.
On the other hand, quality analysis ensures that the software product delivered to your customer matches their expectations. It not just displays diligence in your work but also portrays your brand as authentic. There are many ways that you can accomplish quality testing for your software. However, using Python development for the task is one of the best practices and guarantees superior results.
The Need for Automating Quality Analysis
Quality analysis of your software product can be done in two ways- manually or through an automated way. While the manual method for automation is what organizations have preferred for a long time, things are beginning to change. There are several disadvantages of manual quality analysis that its automated version can overcome. But whichever method companies use, it is crucial to perform a quality analysis no matter how confident they are about their product.
When software is created, it is prone to a lot of bugs. However, it’s not heartbreaking if you are willing to test it and rectify the bugs that appear. Be it the developer’s coding style, missing parameters, or compilation of different modules. Quality analysis ensures that any bug in the software is easily analyzed. Once it is, it can be sent back to the developers for fixing. The point is that the entire QA process assures the quality delivery of products to the clients or customers.
Similarly, quality analysis ensures that your business is on the right path to growth, and you do not end up with a bunch of unhappy customers. When you deliver flawless quality products to your customers, they are bound to come back to you with more requirements. This way, you get more work opportunities and develop a reputation in the market.
Even though you might not see a direct relationship, but quality analysis helps in spreading the right word of mouth among your customers. The point is no matter what you deliver; customers are going to talk about it. So, it’s better to offer them the highest quality that you can, so that what spreads through your mouth is nothing but success stories.
Difference between Automation and Manual Quality Analysis
While some companies say that manual testing is the best way to ensure high product quality, others rely on automation for the job. But, both have their pros and cons. While automating all test cases might sound like a perfect solution, it isn’t the case. You can fire a thousand inputs and test an entire application within seconds. But the fact is that software applications are built for humans, not machines. As humans, we interact with apps in a unique number of ways, which is vital to be considered during quality analysis. The most important bugs are found when interacting with the application manually.
Running a script over and over again does not show or highlight the bus that arises on account of the usability of an application. But, with advancements in technology, the performance of a software application can be easily tested, and quality can be assured. A large part of organizations believe that testing is only manual, but most of the technical and repetitive stuff can be automated using Python scripts. One of the most efficient practices of ensuring quality is to automate some scenarios in the unit test level, some of the API level, some of the UI level along with testing other scenarios manually.
When it comes to automating the quality analysis, there is nothing better than Python that can do the job. Python is not just a robust programming language, but one of the most flexible and easy to use languages that ensure the highest quality of software applications, when used in QA.
One of the advantages of using Python for quality assurance is that it offers a plethora of relevant framework for the task. ‘Pytest’ is one of the most popular quality assurance frameworks of Python. It helps in testing almost anything and everything from basic scripts to databases and APIs. It also lets you test UIs, offering a lending hand in manual testing.
Pytest can be easily installed from PyPi using the command ‘pip install pytest’. Once installed, it can be called in the project using ‘py.test’. Unlike other Python frameworks, Pytest looks for test files in all locations inside the project directory. In other words, any file starting ‘test_’ or ending with ‘_test’ is considered a test file in the Python terminology.
Pytest provides a much simpler syntax to analyze the quality of your application. For example, the default ‘asset’ statement comes handy as compared to other frameworks. Along with this, there are plenty of other customization that come with the Pytest framework. One of them is called sub-string matching. It helps in testing only the selected method from a particular class. This helps in assuring the functioning of even the smallest element in an application. Similarly marking is another method that can be used to run a specific set of tests.
Python for quality assurance is the key to the growth of your organization. With its parallel processing feature, Python lets you run several test methods in parallel. The abundance of frameworks and libraries in the language makes it a boon for both static and dynamic testing landscapes.