What is the role of machine learning in software testing?

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 perform. Then came test automation, which relies on software tools to run tests and identify bugs.

Automation has revolutionized the testing process and brought 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 had a remarkable impact on 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. Improve automation testing

Quality assurance engineers spend a lot of time testing to make sure new code doesn’t destabilize existing working code.

As new features are added, the amount of code to test increases and can overwhelm already overworked QA engineers. In this scenario, manual testing is not the best option because it is time consuming and prone to errors.

However, using tools for automated testing can be useful, especially if the tests need to be run multiple times over an extended period of time. And this is where the real power of AI is manifested.

Through machine learning, AI robots will evolve with the change of code, thereby learning and adapting to new functions. When these bots detect changes in the code, they can easily decide whether it is a bug or a new feature.

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

2. Reduced user interface based testing

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

Additionally, AI-based techniques can also be applied to application logs such as production monitoring system logs to aid self-repair and bug prediction. When used correctly, AI / ML-based techniques can help reduce costs, errors, and overall test time.

3. Help with API testing

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

The tests ensure that the requests are processed successfully, that the connection is stable, and that the end user gets the correct output after interacting with the systems. API testing automation allows users to come up with multiple API quality assurance cases and evaluate the functionality of multiple third-party tools.

And this is where AI comes in handy. Artificial intelligence algorithms help analyze the functionality of connected applications and create test cases. By analyzing large datasets, AI can quickly assess whether the API is working properly and identify areas potentially at risk.

4. Improve accuracy

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

Automation testing helps remove these human errors.

And with the advent of AI and machine learning in software testing, repetitive tasks are handled more efficiently and accurately. Additionally, the use of AI eliminates the likelihood of human error and increases the possibility of finding bugs.

The bottom line

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

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