Artificial intelligence (AI) isn’t a new concept anymore. It has become a big deal in our tech-driven world. From virtual personal assistants to self-driving cars and even in healthcare, you can find AI everywhere now. According to Forbes, the AI market is estimated to expand at a CAGR of 37.3% from 2023 to 2030.
This isn’t astonishing, given the vast number of benefits AI offers like:
- Reduced human errors and better quality
- Improved productivity due to 24X7 availability
- Efficient automation to reduce repetitive tasks
- Enabling a faster decision-making process
As a result, AI has become an essential part of the software testing process, too. It allows testers to perform repetitive tasks in lesser time while ensuring better accuracy. This helps to release software to the market on time, ensuring customer satisfaction.
In this blog, we will cover how AI is used for software testing in detail. We will further discuss the areas where testing teams can efficiently use AI to release bugless software for their users.
Why is AI in software testing important?
Now that you have a basic idea about the importance of AI in software testing, let’s look more closely at each benefit.
- Efficiency – One of the big advantages of AI in software testing is its ability to reduce the time spent on testing. AI algorithms can quickly run many test cases, helping organizations release software faster while maintaining quality.
- Accuracy – AI is less likely to make mistakes compared to humans when it comes to repetitive tasks. So, when we introduce AI into the testing process, it boosts the overall accuracy of tests, reducing the chances of error.
- Accessible – The most popular AI tools are codeless tools that anyone, regardless of their skill level, can use to automate even complicated tasks with easy-to-understand English commands. These tools have user-friendly interfaces that make it easy to run test cases.
- Early Detection – AI in software testing can be included in the DevOps and CI/CD pipelines, allowing continuous testing throughout the development process. As a result, errors are found early on and can be fixed.
- Improved Automation – AI can be programmed to recognize whether a change in the code represents a new feature or a flaw that has emerged from earlier code. AI is able to develop, adapt to coding changes, and discover new functions.
- Scalability– AI-based testing tools are capable of handling large and complicated applications, making them appropriate for projects of any size or complexity.
- Cost Reduction– Automation of testing tasks enables businesses to cut labor expenses associated with manual testing and accelerate the time it takes for their products to reach the market.
How can you use AI in software testing?
AI can help with the following software testing tasks so that you can benefit from its capabilities. Let us take a look at each of these tasks in detail:
- Generate Test Cases – AI can develop test cases automatically based on requirements, code analysis, or historical data, ensuring thorough test coverage. Further, AI can assist in finding undiscovered flaws and vulnerabilities in test cases before they become bigger problems.
- Performance Testing – Artificial intelligence (AI) can simulate user behavior and load patterns to find performance bottlenecks and recommend improvements.
- Automating Workflows – By automating all routine operations, AI solutions can save you time to concentrate on other essential testing chores.
- Maintenance of Test Cases – With the self-healing feature, AI-based software testing tools can automatically detect and resolve the issues arising in a test case due to code changes.
- Testing Before Releases – AI-based testing tools allow testers to decide what tests should be run before different kinds of releases as per the code changes and new features.
Which is the best AI-based software testing platform?
With so many AI-based testing tools in the market, choosing the one that meets your project requirements cost-efficiently can be hard. To save you from all this, we have done our research and found that TestGrid, an AI-powered codeless testing platform, can help you achieve your testing goals.
With its easy three-step process: paste, click, and automate, you can switch to automation very easily. All you have to do is write test cases in BDD, CVV, or English and paste them on their AI portal. Once the test cases are generated, you can automate test scenarios into automation steps and run them on various browsers and mobile devices.
To summarize this blog, AI in software testing can transform conventional ways to software testing because it offers advantages discussed above. The tests you should prioritize for the best results can be identified with the help of AI, which can discover flaws quickly.
With TestGrid, you can save time in test case generation, execution, and bug detection to release software on time. You can save up to 70% of labor costs associated with manual testing. Moreover, your team can focus on crucial business tasks that require human intervention as they don’t have to perform repetitive tasks during testing.
So, try this platform by getting a demo and speed up your testing to release applications that boost your reputation and brand image.