Impacts of AI on Software QA

A lot of recent technological developments have seen the increase of AI and Machine Technologies in the QA process. There are even some major improvements for using AI to test software. However, it can also bring some problems to your QA process if you really too much on AI testing. It should not be viewed as a method to replace people in the testing process. Instead, it should be an additional tool to enhance your best resource – your employees.

Using AI can make the testing process much more efficient. It can be used to take over a vast amount of test execution. One of the major improvements is the reduction in making mistakes. Even experienced QA testers can miss certain defects on projects with large amounts of data. Whereas, using AI testing to wade through seas of data is more efficient and effective.

New Role for a Software Tester 2020

Some of the major benefits for AI with software is the ability for systems to learn analysis and to reapply that knowledge on future tests. This provides more accurate results and also removes the human probability error. Test times are shortened and defects are identified more quickly. This reduces the workload on your QA team by reducing the amount of data they have to analyse.

The new role for software testers is a promising one as AI testing will make the process more efficient. It will take over the majority of test execution. There will also be a requirement for software developers to develop new skills. Int the near future, there will be a requirement for testers to have AI competency and understand algorithm analysis as well as other skills.

There has been a surge in jobs around areas of AI QA and test building. As a result, being an expert in AI will be a great advantage as it is the direction the market is going. Hopefully soon testers will be able to put the majority of their focus on the outcomes of tests and user testing. This will lead to more high quality products going to market. As well as reducing the bugs in products.

Challenges for AI in Software Testing

AI suffers from some problems of course. As with any new technological advance it faces some barriers to implementation. A lot of QA organisations struggle to approach AI technologies. Teams don’t know how to implement AI without building up expertise to merge AI within the business lifecycle.

AI requires some improvements before implementation. There are issues with AI dealing with unstructured data and it may cause issues without additional human resources.

There is already a high demand for software development and QA within the industry. The increasing use of AI will result in broadening the skills required by testing. It may prove difficult to find employees with the expertise in AI development to integrate, maintain and improve it within your business structure.

Interested in AI testing? You can drop us a line here at Code Factory.