Apply AI to Migration for Rapid Results

Artificial Intelligence is the new buzz in this ever-changing tech-world and it has proved to be efficient in several industrial sectors. It is expected that the global market of AI will get almost doubled in the coming years and it might reach up to as high as 60 billion USD by 2025. With such a huge growth, it becomes enviable for businesses and enterprises to know few facts about AI and how it will affect them in providing accurate and rapid results.

Software Testing with Artificial Intelligence

Quality assurance and testing are the two essential pioneers for providing reliable software solution and AI is changing the way software testing and QA is performed. A majority of QA experts suggest that the importance of AI in QA and testing is going to rise as most of the enterprises will adopt this technology in the future.

Artificial Intelligence and machine learning are the two powerful ingredients that help in understanding the input data to be tested. It relieves the pain of undergoing test case generation, optimization, migration, automation execution, defect prediction and result in analysis. Several human tasks can be reduced to extract maximum productivity and ROI through thus AI driven automation.

Simplified Test Case Migration using AI

Most of the enterprises today have piled up test cases owing to the fact that they have data in multiple formats. This increases the time of test case optimization and migration becomes error-prone.

The methods used for test case migration are fully automatic thus leading to a zero error testing. Enterprises can simply migrate the framework assets without any special AI experience.

Automation with AI

Complex software testing may result in delayed deliverables and inefficient solutions. However, Artificial Intelligence can be used to carry out testing automatically with reduced efforts. Still, a majority of software enterprises and QA teams haven’t realized the true power of AI and they are still indulging in the manual testing methods leading them to a long, error-prone route. AI can reduce the time for QA and redirect them towards more strategic goals of improving customer engagement.

Visual Validation using AI

Machine learning based visual tools can spot the differences that the human eye cannot. A simple machine-learning test is enough to detect all the visual bugs in the software. This means it relieves testers from the stress of visual correctness of an application.

Reliable Automated Tests with AI

One common reason for a test failure is a developer making changes in the application; for example, renaming a field ID. The present AI and ML tools come with the ability to learn about the application, relationships between the parts of documents and changes made over time. Such tools can identify application changes and automatically make decisions at the runtime.

This was one of the reasons the testing tool company- Mabel developed a machine learning based algorithm. Mabel was created keeping in mind that small changes in UI should not affect the test by developing a smarter way to handle front-end element changes.

Spidering AI

The most popular AI automation today is writing tests through spidering. There are AI tools available to begin crawling through your application and collect data automatically. Such tools collect screenshots of features, download HTML page, measure load time, and do a lot more.  These steps are repeated time and again. This enables comparing the present state with all the earlier patterns learned over a time. If it learns any changes, then it is flagged as an issue.

AI for Rapid Results

Continuous testing may result in a lot of test data leading to inconsistency. Tools enabled with AI and machines learning can help enterprises in finding out the exact number of tests to be performed owing to the changes in the code. This reduces the time to search out the common patterns over time. Few enterprises today are leveraging the benefits of modern AI tools.