Software quality is extremely important for business in the competitive landscape, as it improves the customer experience. However, despite years, are still prone to errors, and over 60% occur due to a wrong selection of test scenarios.

AI is the future and holds immense power in the testing industry, including regression testing. Incorporating AI for enables software quality assurance, identifying minor unnoticed bugs and accelerating the testing process.

In this blog, we will delve into the limitations of traditional testing, the benefits of AI in regression testing, and the application of AI in the regression testing process.

Exploring AI In Regression Testing

AI is the future, as it imitates human actions and intelligence. In addition, (ML) algorithms enable a learning curve for computers without human intervention. It gathers the data and makes the decision based on a pattern.

These capabilities enable better decision-making while choosing the test suites and test coverage. Moreover, introducing AI & ML in regression testing automation reduces repetitive tasks. It identifies the repetitive tasks and finds the solutions from the past, aiding QA in achieving efficiency.

Common Limitations In Traditional Automation Regression Testing
Unable To Prioritize Test Case

Automation testing can not completely replace manual testing, so they come together to provide better results. However, businesses face challenges prioritizing the test case: which to automate and which to perform manually.

Moreover, if you are dealing with a system undergoing frequent changes, regression testing automation might not help you, as test scripts may fail after each update.

Choosing The Right Tools
Various tools are available in the market, such as open-source and codeless. This is one of the challenging tasks for the QA team, as choosing the trending tool can't always work for your business.

You might look for a tool to reduce time and effort, offer better test script maintenance, and create useful reports where non-powered AI automation tools have failed.

High Maintenance
In traditional automation, the QA team must have an understanding of the programming language to create test scripts. Moreover, these scripts are difficult to maintain, as test scripts can fail after any update. It means testers need to update the defective test scripts.

More Focus On UI Testing
Usually, traditional test automation focuses more on UI testing, involved in interacting with other user interfaces of applications. However, UI testing can be challenging, and focusing on this can lead to negligence in testing.

The Benefit Of Incorporating AI In Regression Testing

Increased Efficiency
Businesses need to perform regression testing after each update in code, which can be time-consuming and require more effort. AI-based regression testing automation can perform testing in less time, ensuring the software quality.

AI & ML gathers the insights and automatically creates test scripts, saving software 80% of the time getting on repeated tasks. So, AI reduces manual effort, accelerates software development cycles, and makes regression testing more efficient.

Improved Accuracy
AI-based testing can detect minor problems that can go unnoticed with traditional automated testing tools. AI identifies the root cause of failure and helps make testing procedures more effective & precise. Moreover, this also aids in the agile software development cycle.

Cost Reduction
AI-powered testing tools can complete tasks more quickly and regularly than the traditional testing method. AI software allows you to automate repetitive tasks and save the money that can be spent on QA for manual testing.

Scalability
After a release or update, businesses need you to update the test case effectively. However, the increase in demand leads to more complexity of applications, where traditional automation fails to provide more coverage. AI can easily make changes in the UI and adapt accordingly.

Application Of AI In Regression Testing

Test Case Generation

AI easily generates the test case based on user data, requirements, previous information, and code base. Moreover, AI prioritizes the test case, essential for optimizing the regression testing automation. With AI, businesses can save time on test case generation, maintenance, and execution, reducing the testing time.

Test Scripts Maintenance

can be challenging, as continuous tests can result in errors. AI can quickly identify the outdated test case, and it can generate or update the test case to maintain the test suite. Moreover, it also enables more coverage and gathers insight to identify the potential threat.

Test Data Generation

AI generates test cases that are helpful with various scenarios and detect potential vulnerabilities. AI-powered testing tools analyze the collected data to identify which test suits are more efficient. It strengthens the relationship between the test case and code change for a better solution.

Defect Prediction

There is a high chance that a few bugs or errors can go unnoticed, which can be challenging in the future. Hence, AI enables defect prediction analysis, predicting the bugs before they have occurred.

Opkey: The Best AI-Powered Tool In The Market

Opkey is an AI-powered testing tool that aids regression testing automation, removing all the barriers. One can perform regression testing without any coding language with Opkey's drag-and-drop interface. It enables self-healing functionalities, which allow for self-fixation in test cases after any update or release.

Moreover, with Opkey, you can reduce the testing time and improve the software development cycle. Visit Opkey's website and leverage AI-based regression testing.