Software testing is an essential part of the software development lifecycle (SDLC). Initially, testing was done manually—a process that took a lot of time and effort to execute. Then came test automation, which leverages software tools to run tests and identify bugs.

Automation revolutionized the testing process and brought along many benefits such as faster feedback and higher test coverage.

Today, machine learning (ML) and artificial intelligence (AI) have entered the software testing space, redefining a new era in the software development industry. AI in software testing aims to make testing smarter and more reliable.

AI and ML have made a remarkable impact in software testing, where their implementation has made the testing process easier, faster, and more accurate. This article will explore the role of machine learning in software testing.

1. Improving Automation Testing

Quality assurance engineers spend a significant amount of time performing tests to ensure new code doesn’t destabilize the existing functional code.

As more features and functionalities are added, the amount of code to be tested expands and can overwhelm the already overburdened QA engineers. In this scenario, manual testing isn’t the best option as it’s time-consuming and prone to errors.

However, using tools for automated testing can come in handy, especially if the tests need to be run repeatedly over an extended period. And this is where the true power of AI manifests.

Through machine learning, the AI bots will evolve with the change in the code, thus learning and adapting to the new functions. When these bots detect modifications to the code, they can easily decide whether it’s a bug or a new feature.

Moreover, instead of running an extensive test suite to detect a minor bug, AI will run specific test cases on a case-by-case basis, further speeding up the testing process.

2. Reduced UI-Based Testing

Another transformation brought about by AI/ML testing is automation without the user interface. AI-based techniques can be applied for non-functional tests such as unit integration, performance, and security.

Additionally, AI-based techniques can also be applied to application logs like production monitoring system logs to help with self-healing and bug prediction. When used correctly, AI/ML-based techniques can help reduce the cost, errors, and overall testing time.

3. Assisting in API Testing

API evaluations allow developers to evaluate the quality of interactions between different programs communicating with servers, databases, etc.

Testing ensures that requests are processed successfully, the connection is stable, and the end-user gets the correct output after interacting with the systems. Automating the API testing allows users to come up with multiple cases of API QA and assess the functionality of multiple third-party tools.

And this is where AI comes in handy. Artificial intelligence algorithms help to analyze the functionality of connected applications and create test cases. By analyzing large data sets, AI can quickly assess if the API is performing correctly and identify potentially risky areas.

4. Improving Accuracy

To err is human. Even the most experienced testers are bound to make mistakes, especially when performing monotonous tests.

Automation testing helps to remove these human errors.

And with the advent of AI and machine learning in software testing, repetitive tasks are handled more effectively and accurately recorded. In addition, the use of AI eliminates the probability of human error and increases the possibility of finding bugs.

The Bottom Line

AI-based testing approaches employ reasoning and problem solving to automate the software development process better and improve testing.

Employed in large-scale testing, AI helps to eliminate manual processes, thus reducing the time spent in testing. This allows QA engineers and developers to focus on more important tasks, like creating new, innovative features.