Home Digital Marketing Machine Learning (ML) and Artificial Intelligence (AI) in App Development

Machine Learning (ML) and Artificial Intelligence (AI) in App Development

The seamless and effective collaboration between users and their favorite apps is made possible by the efforts of the QA. These troubleshooters work tirelessly to guarantee that every element of the applications, which countless users rely on for their daily requirements, operates flawlessly across all releases and updates.

Machine Learning (ML) and Artificial Intelligence (AI) in App Development

In today’s landscape, the demands of users, which encompass nearly everyone, are nothing less than perfection. Therefore, the QA team’s objective is to guarantee a faultless product in every deployment, leaving no room for errors. For more information about software development, visit Lampa.

The presence of bugs and problems can quickly wreck an app. Unfortunately, ensuring that there are no bugs is very difficult. And it’s only getting more complicated. Today’s software development world is getting more complex every day, and making checkings on the many potential features and scenarios that this complexity brings with it is also becoming more complex and resource-intensive.

Let’s remember how we got to this point.

Manual Testing in 1980

In the not-so-distant past, teams heavily relied on manual inspections of their devices to ensure the smooth functioning of the products. This era was characterized by simpler devices with limited capabilities and operating in fewer scenarios, making manual testing an adequate method for verification. Despite being a time-consuming and laborious task when testing was satisfactory for the enthusiasts of that time.

But technology, as a constantly growing and advancing organism, has brought changes in the form of automation, which has greatly improved the testing process.

Test Automation With Coding From the 1990s to 2010s

As time progressed, significant strides in testing methodologies brought a new level of freedom. Professionals were no longer burdened with the laborious task of manually navigating through test cases, desperately searching for bugs. A solution arose: the recognition that relying solely on testing had become unfeasible. To ensure thorough evaluations of potential releases within reasonable timeframes, the professionals embraced the adoption of automation tools. By leveraging test scripts and automation, they effectively streamlined the testing process, enhancing efficiency and accuracy.

Was the war on complexity won this way? Not really. It may be better to view automated checkings not as a revolutionary innovation, but as another step in the arms race against the ever-increasing complexity of software.

Time passed, and there was no clear victory on the horizon. Automated testing was losing ground as mobile apps grew in popularity and became tools central to our everyday lives.


Test Automation Without Coding in 2020

Until very recently, the situation of the QA testing team was quite dire. Automated checkings required an increasingly complex coding apparatus to ensure a quality product release, so more and more programmers had to be allocated specifically to check up rather than to other tasks, such as creating new features. Not only was this becoming increasingly costly, but it also meant pushing release dates further and further into the future. But the alternative – a disaster at launch – could have cost much, much more (as evidenced by many high-profile release failures).

But then came the inevitable thanks to the principle of abstraction when interface-based representations overwhelmed the incredible complexity of the processes. Many experts have long heralded the emergence of a new level of abstraction, the ‘No-Code Revolution’, and in the last few years, this has indeed happened.

Several platforms have emerged that enable the use of no-code solutions in a wide variety of industries. One of the most notable examples is the popularity of WYSIWYG website editors (think Squarespace or Wix). The no-code revolution has brought about sweeping changes, allowing non-specialists to tackle complex tasks and freeing up specialists for other tasks. Undoubtedly, we will see more and more no-code solutions for a variety of industries shortly.

Machine Learning (ML) and Artificial Intelligence (AI) in App Development

Really Intelligent Self-Testing Software

The no-code revolution is just another step forward, and the next step in software checking will be software that tests itself. Given the pace of change and technological development, it is conceivable that by 2025, intelligent test automation (i.e. self-testing software), where a testing AI operates without human intervention, will expand significantly.

Already today, even limited adoption of intelligent testing increases the speed and quality of software release by relying on machine learning and AI platforms. This allows for rapid and continuous testing (and therefore a higher return on investment). AI can duplicate human intelligence, while ML makes machine learning possible without human intervention.

AI and ML use deep learning algorithms to access and learn from data by extracting patterns for more efficient debugging and decision-making. With this technology, QA teams can run multiple tests on a wide variety of devices of different form factors.

No code does not mean any people, and people are not machines: they make mistakes. Even with a much-reduced amount of coding, the human factor is still a factor that can cause serious problems. 

Intelligent testing automatically generates and maintains cases and provides valuable benefits that can be summed up as increased productivity and quality of results. But for intelligent test automation to become the norm, the following components must first be combined: 

  • Training based on human behavior. When the machine tests, it has to act like a human being. It has to learn what the human needs and wants, and how it behaves with the device. This can be difficult to predict, and as we said before, a complex application means complex scenarios and testing patterns. 
  • Learning from real product usage data. The machine must understand how the application is used in different environments. This includes understanding what devices can be used, what language is installed on the device, and usage streams, including the use of menus, screens, and actions.
  • Training data. As with autonomous vehicles (a nut that still hasn’t cracked), ML requires data for learning, which helps define software patterns.

These three elements must be mastered and thoroughly tested for each code change. They must be combined and prioritized organically and intelligently.


Software self-testing is just one example that is now emerging on the market, the development of no-code towards machine learning. Creating entire websites based on a few user-defined parameters is also a matter of time. Great changes will come soon and we become witnesses of this new era.