Artificial Intelligence, Machine Learning, and the Future of Software Development

Every successful interaction between you and your favorite apps is the result of a joint effort by the QA team.

Every successful interaction you have with your favorite apps is the result of a collaborative effort by the Quality Assurance (QA) team. These tireless problem hunters ensure that every aspect of the applications that mobile device users around the world rely on for their daily needs runs smoothly with every release and update.

When you wake up to the sound of your alarm in the morning, check the weather, or send a message to a loved one, we must give thanks to these often unsung heroes.

When the team's efforts failed, they were sure to hear the news: Many users didn't hesitate to provide negative feedback on popular (and very obvious) review sites.

The expectation of modern mobile app users - which is pretty much all of us these days - is perfection, and the primary goal of QA teams is to ensure every deployment of a bug-free product.

The presence of bugs and issues can quickly sink an application. Unfortunately, ensuring a bug-free experience is no easy task. And it's only getting more complicated. Today's software development world is becoming increasingly complex, and testing against the many potentials and scenarios that this complexity brings means that testing itself is becoming increasingly complex and resource-intensive.

Given the history of mobile application development, it is quite reasonable to expect that applications will only become more complex and require more advanced and frequent testing. But does it have to be this way? Are we destined to need more and more staff and bigger and bigger QA teams?

The 1980s: Manual Testing

Let’s take a moment to consider how we got here. Until recently - "Wow - I guess this was really the 1980s so long ago" - software QA teams relied heavily on manual testing of their equipment to ensure the products they brought to market performed well.

It was a simpler time when devices had far fewer features and active scenarios, so hand coding was an adequate way to test. Although tedious work takes a lot of time when performed thoroughly, manual testing works well for testers.

But technology, being an ever-evolving and improving beast, has ushered in changes in the form of automation, dramatically improving the testing process. Software continues to advance and become more complex.

1990s – 2010s: Coding Test Automation

Over the next few decades, advancements in testing freed QA testers from the requirement to physically walk through test cases. They no longer have to manually find bugs in spaghetti piles of code.

They have a new weapon in the war on software issues: manual testing at scale has become impractical, and if any QA team is going to thoroughly test a potential release in a reasonable amount of time, they need to The following is an automated tool for executing test scripts.

So, is the complexity war won? Not completely. It's best to think of automated testing less as a revolutionary innovation and more as another step in the arms race with the ever-evolving complexity of software.

The clock is ticking, but there is no clear victory on the horizon. As mobile apps gain popularity and become a core tool in many of our daily lives, automated testing is slowly losing ground. Fortunately, a long-awaited change is coming, a real revolution.

2020s: Codeless Test Automation

Until recently, the dilemma of QA testing teams had become quite dire indeed. To ensure high-quality product releases, automated testing requires increasingly sophisticated coding tools, which means QA teams need to dedicate more and more programmers to testing instead of other tasks, such as generating new features. Not only is this increasingly expensive, it also means pushing release dates further and further back. But the alternative, a catastrophic launch, can be much more expensive (as many high-profile failed launches have proven).

But the inevitable happened. Through the principle of abstraction—interface-based representation paves the way for extremely complex processes (think, for example, of the 1s and 0s hiding behind the article you are reading)—many experts have long heralded a new layer of abstraction, one that There’s a “no-code revolution” that has really come to fruition over the past few years.

Several platforms have emerged recently that allow for the use of no-code solutions in various industries. One of the more obvious examples of the no-code revolution is the popularity of true WYSIWYG website editors (think Squarespace or Wix), and in the less obvious area of ​​software testing, the company I founded, Sofy, is a unique platform , which provides code-less testing for mobile applications.

The no-code revolution has brought about sea-changes, allowing non-experts to handle complex tasks and giving experts more time to handle other tasks. Therefore, we will undoubtedly see more and more no-code solutions for various industries in the near future.

2025? Truly Smart Self-Testing Software

That said, in the scheme of things, the no-code revolution is just another step forward, I believe the next step in software testing is to test itself software.

I'm not alone in this: Like the no-code revolution, self-testing software has been an expected reality for years. At the rate at which technology is changing and growing, it’s not absurd to imagine that by 2025, intelligent test automation (i.e., self-testing software) that can test AI operations without human intervention will be greatly expanded.

Currently, limited implementations of smart testing improve the speed and quality of software releases by relying on machine learning (ML) and artificial intelligence platforms. This allows for rapid and continuous testing (and therefore improved ROI). Additionally, AI can replicate human intelligence, while ML allows computers to learn without human intervention.

Artificial intelligence and machine learning employ deep learning-based algorithms to access data and learn from the data by extracting patterns for more efficient debugging and decision-making. Additionally, this technology allows QA teams to perform many tests across a variety of devices and different form factors.

Not days, but hours. Nowthis isa revolution.

No code still requires people; people are not machines: they make mistakes. Even without code—albeit greatly reduced—human error is still a factor that causes serious problems. Consider the overuse of resources, time, and effort caused by manual testing.

Smart Testing automatically generates and maintains test cases and generates valuable benefits that can be summarized as increased productivity and output quality. But to achieve intelligent test automation, you must first combine the following elements:

• Learning from human input: When a machine performs testing, it must act like a human. It must understand what humans need and want, and how humans use devices. As we discussed, this can be difficult to predict, and complex applications mean complex testing scenarios and patterns. However, the machine must be understood and operated from this vantage point.
• Learn from real-life product data: The machine must understand how the application is used in different production environments. This includes understanding the device that may be in use, the language it is set in and the flow of its use, including the use of menus, screens and actions.
• Training Data: Like self-driving cars (a nut that has yet to be cracked), machine learning requires training data to help outline software patterns.

These three items must be internalized and tested thoroughly for every code change. They must then be aggregated and prioritized in a seamless and intelligent manner. This is no small feat, but we will continue to work towards the next step.

We don’t have that yet. Each of these steps must be completed before we can move forward, but it's really just a matter of time.

Self-testing software is just the first step: I predict we can expect other no-code examples just hitting the market in the direction of machine learning. I believe it's only a matter of time before generating entire websites based on some user specified parameters becomes a reality. Today, the no-code revolution has finally arrived, but with it comes the beginning of another revolution.

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