New Technologies Require New Testing Mechanisms

New Technologies

A new set of software testing challenges

We all know that the IT and software development industry is growing at such a rapid pace that makes it imperative to keep all practices up to date. And as quickly as the software is being developed, testing should go hand in hand with the process, to ensure that the demand is met not only in terms of the timeframe but quality as well.

As a part of a custom software development company, I observe firsthand how new testing mechanisms are introduced in order to keep up with the process. 

The increasingly agile software development

Agility refers to speed and efficiency. And it is clear why so many software companies choose to operate on a variation of the Agile business model. Both speed and efficiency are of essence – without them, a product cannot be conceptualized, developed, tested, debugged or optimized on time to meet the release requirements.

Big data and the Internet of Things

Creating relevant, reliable and high-quality products rely greatly on the efficiency of the project as well as making the right decisions based on the available information. That being said, Big Data and AI infiltrating the practice, this becomes more and more of a demand rather than an isolated case.

The ability to go through vast amounts of data in a short time frame, as well as extracting relevant conclusions that benefit the business and the development process is a must. As a result, relying on the automated process is the future of testing.


Confidentiality in regards to the development process is a must to be able to market and sell a software product. Products are developed and released on the market in a matter of months, and yours needs not only to stand out but be secure for the users.

Testing not only functionality but also how secure a product is, can make the difference in terms of usability, sales and also how the maintenance of the product is done once released. New approaches for software testing


Making the most of the efforts of developers relies heavily on the processes that are set in place. Using automation is not only recommended but essential for the DevOps process. As of now, roughly 20 per cent of the development and testing process is relying on automated processes. So there is clearly a lot more room to grow from here with the rise of Machine Learning and AI as a part of testing. 

Artificial Intelligence and Robotic Process Automation (RPA)

Artificial intelligence as a practice in development is hardly new, but the influence it has is only growing. It provides a quicker, more reliable solution to challenges, that, when tackled the old-fashioned way, takes hundreds of hours off competent developers who could instead take strides in development. Processing and analyzing large amounts of data coupled with the predictive algorithms guide the process with a stronger foundation, while also allowing for flexibility where it is needed.

Utilizing the most of AI and ML allows understanding test coverage as well as zero in on any potential pitfalls.

Integration of tools and activities 

Testing needs to be backed by the right tools. The existing automation tools are still continuing to adapt in order to provide the best service that is needed from them. Adding new features to tools such as Selenium, Test Complete or Katalon make the process more and more effective.

The potential setback when it comes to using such tools is the integration with other tools that assist the project management from start to finish. The tool must be able to assist all aspects of the application and also support AI/ML-based solutions.


As far as testing has come, the demand for growth is inevitable. The tools and the methods used must match the pace of the industry as well as integrate and make the most of the advancements in the tech industry.

What is a successful testing approach that your team has implemented? What do you believe needs to be built as a practice to improve the process?

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