The Impact of AI on Software Testing

Understand why AI matters in software testing, the benefits it offers, and what are the most common challenges companies face while using it.

The Impact of AI on Software Testing

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Software has always been in a state of evolution, ever since its inception. There are two aspects to examine the evolution of a software: outer and inner. The outer aspect is about the objective that the software can achieve. The inner part is about how it was developed and tested. Both outer and inner aspects go hand in hand. Earlier software was much simpler. They used to fulfill a specific purpose, which was mostly simpler in nature. Those functionalities could be achieved through a simple development process. Hence, if the code and logic were simple, their testing had to be simple as well. Which means, traditional software with its traditional development could manage with manual testing.

But as the software became more complex, its development became complex too. In that scenario, manual testing proved to be insufficient, both from a scalability and efficiency point of view. Hence came the power of automation. Automated testing was one step above manual testing and was able to fulfill many functions that traditional testing lacked. Yet, it was not sufficient. It still needed human involvement for writing test scripts and executing them. That became a bottleneck for the next generation. Thus, finally, AI-based testing. It is more intelligent and adapts to the application’s testing needs. In this article, we will specifically explore the impact of AI on Software Testing, its benefits, and discuss how to make the most out of it.

Why AI Matters in Software Testing?

To understand this, let’s go one step behind. When the internet became a global phenomenon, it spread across all the domains of business and public life. Revolutions like the internet, or even the industrial revolution, which came much before that, are so big that their impact diffuses around all corners of life. When the internet came, people didn’t have a choice but to adopt it. If you didn’t, you would miss the train. Such revolutions change the entire landscape of humanity in just one generation. Software, which was mostly installed on-premises, became cloud-based and started being served as a service. That’s how software evolved from standalone products to Software-as-a-service. Today, it has become so common that a company cannot even imagine launching a software that cannot be delivered through the cloud.

Now, let’s understand the AI revolution in the same context. AI is the next biggest revolution after the internet. It is no longer just a technology that is optional. Nor is it even a luxury. It has become so important that growth cannot be imagined without it anymore. AI is not replacing older systems, but upgrading them massively. All the elements of the software development life cycle are being augmented by AI, including testing. In a modern testing environment, speed, scalability, efficiency, and managing complexity have become indispensable. And that cannot be achieved without AI. That’s why AI matters in Software Testing.

Benefits of AI in Software Testing

So far, we broadly understand why AI matters in software testing. But now, let’s get more specific on its benefits that include:

Accelerated Testing

Companies that use AI-based testing tools have been able to shrink their testing timeline to a much smaller degree. The reasons are obvious. Most of these tools are automated and intelligent enough to facilitate the testing process much faster. They reduce execution time and regression tests, which are most common and repetitive, can be done in minutes.

Automatic Test Case Generation

Modern AI tools have an abstraction layer of AI above raw code that was previously used for writing test scripts. This layer takes input from humans in simple language and converts it into the code that gets executed for generating test cases. This whole process happens instantly, the moment a user provides input and hits run. All you need to do is to integrate your testing tool with the application and give commands to its GenAI engine that generates test cases. Out of many options available today, testRigor, as a software testing automation tool, specializes in this feature the most.

Predictive Analytics

This feature is what differentiates intelligent software from traditional ones that operate on hard-coded rules. Predictive analytics feature enables the testing tool to forecast future bugs and highlight high-risk areas. It does so by studying the historical patterns of bugs and applications and notifying the user of the probable risks. Based on that, dedicated testing can be performed to prevent any future bugs.

Self-Healing

Traditional testing methods often faced challenges with test case maintenance. A test case created earlier may not run correctly even when there’s the slightest change in the code. But an AI-based testing tool can self-repair its test cases according to the updated code. Thus, maintenance becomes easier, requiring minimal to no human involvement.

Challenges of AI Testing

Although AI has almost taken over the entire testing industry, it is still in its infancy. There are still a few challenges looming around in this modern approach. These are:

Trust and Transparency

For complex software, when AI suggests some test cases, it becomes difficult to understand their purpose for a simple human mind. It’s because the logic behind those suggestions is opaque. We don’t know the “why” behind AI’s suggestions, and that may turn the process incomplete if we just follow it blindly.

Bias Problem

Any AI system is only as good as the quality of data it was trained on. If the data fed was full of biases and inaccuracies, it would ripple through the outcomes that the AI system generates. E.g., if enough data was not fed to the AI testing tool for creating test cases, it may miss out on generating edge cases.

Over-reliance on AI

Although a positive side of AI tools is that they give more bandwidth to the testers for creative explorations, while delegating the repetitive tasks to the tool. But sometimes, this advantage can also turn into a blind spot. Over-reliance on AI systems drastically reduces human critical judgment. This may allow leakages in the testing, causing bugs at a later stage.

Closing Statement

To dispel the most common myth about AI, it is not going to replace humans. But a tester who knows how to use AI for their testing will surely replace the one who doesn’t. AI can be best viewed as a collaborator rather than a competitor. It is augmenting humans’ capabilities in all directions, and testing is not left out in that. The same principle applies to AI testing as to any other stream - delegating the routine tasks to AI to gain bandwidth for higher tasks. It not only eases the life of testers but also creates an impact on the business. Companies are able to release their products much faster than before. And for those products that often undergo frequent changes, regression testing can be done instantly. Hence, if you are still considering whether to include AI in your software testing process, the best time is now.